An Intelligent Decision-Making Support System for the...

Research ArticleAn Intelligent Decision-Making Support System for the Detectionand Staging of Prostate Cancer in Developing Countries

Jun Zhang,1,2 Zhigang Chen ,1,2 Jia Wu ,1,2 and Kanghuai Liu1,2

1School of Computer Science and Engineering, Central South University, Changsha 410083, China2Central South University, Changsha 410075, China

Correspondence should be addressed to Zhigang Chen; [email protected] and Jia Wu; [email protected]

Received 3 May 2020; Accepted 11 June 2020; Published 17 August 2020

Guest Editor: Plácido R. Pinheiro

Copyright © 2020 Jun Zhang et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Most developing countries face huge challenges in the medical field; scarce medical resources and inadequate medical personnelwill affect the development and stability of the society. Therefore, for most developing countries, the development of intelligentmedical systems can greatly alleviate the social contradictions arising from this problem. In this study, a new data decision-making intelligent system for prostate cancer based on perceptron neural network is proposed, which mainly makes decisionsby associating some relevant disease indicators and combining them with medical images. Through data collection, analysis andintegration of medical data, as well as the disease detection and decision-making process, patients are given an auxiliarydiagnosis and treatment, so as to solve the problems and social contradictions faced by most developing countries. Through thestudy of hospitalization information of more than 8,000 prostate patients in three hospitals, about 2,156,528 data items werecollected and compiled for experiment purposes. Experimental data shows that when the patient base increases from 200 to8,000, the accuracy of the machine-assisted diagnostic system will increase from 61% to 87%, and the doctor’s diagnosis rate willbe reduced to 81%. From the study, it is concluded that when the patient base reaches a certain number, the diagnostic accuracyof the machine-assisted diagnosis system will exceed the doctor’s expertise. Therefore, intelligent systems can help doctors andmedical experts treat patients more effectively.

1. Introduction

Prostate cancer (PCA) is a human disease that occurs inmalignant tumors of the prostate epithelium [1–6]. The ageof its onset is mainly after 55 years of age, and the incidencerate also increases with the increase of age. In Europe and theUnited States, the incidence of male cancer patients rankedfirst, [7, 8] while the death rate ranked second. In America,the incidence of prostate cancer has risen to become thefirst place; the mortality rate is second only to lung cancer.Prostate cancer has become one of the most common cancersin the world and one of the most common malignanciesamong men in Europe and the United States [9].

In developing countries such as [10] China, the incidencerate is lower than that in many European and American coun-tries. However, due to the large population base in developingcountries, the number of cases is not to be underestimated [11].

Before the 1980s, the incidence of prostate cancer in China wasless than 1/100,000, and the incidence was extremely low, butafter decades of change, the incidence of prostate cancer hasnow exceeded 5/100,000, and it is skyrocketing, nearly fivefoldto the original. Specifically, the incidence of prostate cancerregistered in China in 2012 was 9.92/100,000; the sixth highestincidence of malignant neoplasms in men, and in 2018,prostate cancer patients in Asia accounted for 18,100,001 andhalf of the world’s new patients [12, 13].

Nowadays, many developing countries are facing bigdilemmas:

(i) Medical equipment and treatment level are limited, andwith the increase of cancer incidence, it is very easy tocause high misdiagnosis rate if the medical level cannotkeep upwith the rate of growth ofmorbidity. Therefore,a large number of social contradictions may appear

HindawiComputational and Mathematical Methods in MedicineVolume 2020, Article ID 5363549, 18 pageshttps://doi.org/10.1155/2020/5363549

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https://doi.org/10.1155/2020/5363549

(ii) The large population base in developing countriesand the relatively small number of medical institu-tions, as well as medical personnel, have led to animbalance in the number of doctors and patients

The capital of China, Beijing, has a large population andhas nearly 100,000 old people and children. But on thecontrary, there are only 3,000 medical staff serving peoplein this metropolis. As a result, there is an extreme imbalancebetween the size of the population and the level of medical. Indeveloping countries, even the big cities still have so manyproblems, let alone small cities in remote areas. It is usuallymore acute that the ratio of urban to rural population isabout 4 : 6 in 2017 [14], but the city’s medical level is muchhigher than that in the rural.

In developing countries, doctors in hospitals have to beara huge burden because of heavy work and mental stress. InChina, due to the huge population base, each doctor is allo-cated to 5,000 patients on average, while a lot of work pres-sure often leads to high rate of misdiagnosis. Moreover, [15,16] in the treatment of prostate cancer, 640 images are gener-ated for each patient by PET-CT scan. If the screening test isperformed only by manual means, it will be very inefficientand with mental fatigue, working time is too long, and therate of misdiagnosis will also increase.

Therefore, hospitals in developing countries have similardilemmas:

(i) A large number of patients and relatively few medi-cal staff form sharp contradictions, which leads to alarge workload of medical staff, easy to cause physi-cal fatigue and mental pressure of doctors, and even-tually may increase the rate of misdiagnosis

(ii) For example, screening PET-CT [17] image is a taskwith a large number of images and a large amount ofrepetitive work. If handled manually, it will only beinefficient, waste a lot of time, but also will fatiguethe doctor easily, and, last, resulting in a high rateof misdiagnosis

(iii) There is a “generation gap” between doctors andpatients. Patients do not understand the physiologi-cal indicators of prostate cancer, which makes itdifficult for doctors to communicate with patients

Faced with these problems, the article uses an intelligentmedical expert system to improve the above medical condi-tions. The doctor’s final choice can be combined with hisown experience through system-assisted diagnosis to makethe most complete decision-making treatment. In this sys-tem, a neural network-based prostate cancer big data intelli-gent system is adopted. The system assists the diagnosisdecision from two aspects: image and disease index. Byadjusting the weights of the two, the two aspects are finallycombined to determine the clinical stage of cancer and pro-vide appropriate treatment strategies for physicians. Big datadecision-making intelligence system can complete thephased decision of disease quickly and effectively and at thesame time can provide timely and effective treatment adjust-

ment to the patient to ensure effective treatment of the dis-ease. In summary, the contributions made by the researchare summarized as follows:

(1) To construct a newmodel of prostate cancer based onperceptron neural network, which combines a seriesof indicators and related images to determinewhether there is cancer and to determine the severityof the cancer, up to the clinical stage

(2) According to the images obtained by positron emis-sion computed tomography (PET) or computedtomography (CT) taken by the hospital technicians[18–21], the images can be screened, and the mosteffective part of the images can be screened fordoctors’ reference, so as to reduce the burden ofdoctors and improve work efficiency

(3) The intelligent system assists in medical diagnosisand through statistical analysis data can be used asan auxiliary diagnosis and automatically provide afast and accurate treatment plan for doctors

(4) In order to ensure that the learning neural networkcan obtain accurate weights and ensure the accuracyof decision-making, the research is based on2,156,528 hospitalization information of three hospi-tals in China. The experimental results show that theintelligent medical decision-making system canimprove the efficiency of doctors and reduce the rateof misdiagnosis

The rest of this paper is organized as follows: in thesecond part, introducing the current state-of-the-art relatedto our work; the third section proposes and constructs a bigdata decision model; the fourth section gives a detaileddescription of the experimental performance; the conclusionis given in the last part.

2. Material and Methods

The intelligent auxiliary diagnosis system is a new type ofintelligent system based on machine learning and its applica-tion has been recognized by the society to a large extent.Through a large number of precedents, the intelligent systemcan still greatly facilitate people’s lives and reduce the relatedwork and life burden. The emergence of the intelligentmedical system is expected to improve the current medicaland health conditions in developing countries. With theupdate and development of technology, in many countriesand regions, the intelligent medical system can assist indiagnosing of the diagnosis of the patient’s health care systeminformation of patients and to help doctors to do somesimple relative work, which greatly reduces the workload ofdoctors, and even able to diagnose the illness and makecorresponding treatment decisions, making doctors’ to havea more effective understanding of the health situation. Onthe other hand, in order to increase the accuracy and ratio-nality of machine-assisted diagnosis, the article should notonly use simple indicators to make the decision process but

2 Computational and Mathematical Methods in Medicine

also include more important PET-CT images to make thedecision, because sometimes, the images can judge thedisease more directly, accurately, and effectively. Therefore,the article establishes a new decision model based on theperceptron neural networks in developing countries.

2.1. General Framework of the Machine-Aided DiagnosisSystem Model of Prostate Cancer in the Context of Big Data.During the real medical diagnosis of prostate cancer, doctormakes the corresponding diagnosis through a variety ofindicators such as PAP, PSMA, HB, PSMA, TPSA, RBC,PAP, FPSA and patients of positron emission computedtomography (PET) and computed tomography (CT). In reallife, doctors will diagnose and treat according to their ownknowledge and treatment experience.

In a big data environment, our medically assisteddiagnostic systems are also based on big data. Throughmachine learning, a large amount of data is learned anda mature neural network algorithm is constructed toperform machine diagnosis through medical data andimage information. As shown in Figure 1, it is the overallstructure of the big data decision model of the intelligentmedical system, and it introduces the system’s operationprocess and data flow and processing in detail andclearly.

Finally, because of the strong concurrency of the hos-pital’s intelligent system, it is able to process a large num-ber of patient information at the same time. The system ismainly divided into five stages. The first stage is the pre-processing model of medical images before the input of

Parameter-prostate cancer

TPSA RBC HB FPSA PAP PSMA

ACP (t)Packaged intovector

XTPSA XRBC XHB XFPSA XPAP XPSMAInput layer

Wc1 Wc2 Wc3 Wc4 Wc5

Hc1 Hc2 Hc3 Hc4 Hc5

Hiddenlayersinput

Hiddenlayer

𝛽1Output layers

input

ACOutput layers

Area Gray-level

AHP (t)

XArea XGray-level

Wp1 Wp2 Wp3

Hp1 Hp2 Hp3

𝛽2

AP

Packagedinto

vector

Input layer

Hiddenlayersinput

Hiddenlayer

Output layersinput

Output layers

DPC

Case 1 Case 2 Case 3 Case 4

Stage I Stage II Stage III Stage IV

Output DPC and the clinical stage

Treatment recommendations

Adjust theweight of the

two parts

Determine thisclinical stage and

propose treatmentrecommendations

Staging according to diseaseindicators

Staging according toimage indicators

Acasex = [Acase1, Acase2, Acase3, Acase4,] Acasey = [Acase1, Acase2, Acase3, Acase4,]

w2w1

Figure 1: The overall structure of the big data-assisted diagnostic decision model.

3Computational and Mathematical Methods in Medicine

the machine-aided diagnosis system of prostate cancerunder the big data environment. Finally, it achieved twogoals. One is accurate and effective screening of medicalimages. In the current hospital system, the PET-CT scansgenerate about 640 images per patient. If the image isprocessed and screened in a manual form, a large amountof labor, material, and human resources will be wasted. Ifall images are stored and processed, 640 images needabout 2M bytes of space, and only part of the 640 imagescan be used as effective processing information, which willresult in the waste of medical system resources. The otheris to calculate the concentration value of the prostate can-cer lesion area and carry out edge detection and process-ing on the effective images to obtain the correspondinglesion area, which can be used as the input informationof prostate cancer machine-assisted diagnosis systemunder the big data environment.

The second stage is the input model of the machine-assisted diagnosis system for prostate cancer under the bigdata environment. All the obtained digital information willbe preprocessed properly, and the irregular, unreasonable,and invalid information is transformed into qualified andeffective digital information. Six different disease indexes(tumor markers) can be used to evaluate the clinical stagingof prostate cancer produced by two kinds of medical imageindexes: the lesion area and lesion area density; then, thesetwo kinds of data integration generate input vectorACP1andACP2, to the different data models.

The third stage is the neural network model of themachine-assisted prostate cancer diagnosis system underthe big data environment, which is the operation processof the input layer, hidden layer, and output layer of theneural network. The whole neural network model com-bined with the diagnosis process of prostate cancer isdivided into two parts. The first part is a large numberof prostate cancer cases collected and counted in the earlystage as the data information of model training, with thepurpose of constantly adjusting the weight and biasbetween various points. Positron emission computedtomography (PET) and computed tomography (CT)patients with six different disease indicators (tumormarkers): Hb, PSMA, FPSA, RBC, PAP and TPSA, andtwo medical image indicators. The lesion area is the den-sity of the lesion area generated by the two vectors asthe input of the input layer. The disease index and themedical image are finally found to be the return error ofthe two-part model parameter adjustment.

The fourth stage is the decision-making model of themachine-aided diagnosis system for prostate cancer underthe environment of big data. The final output value isobtained by combining the prostate cancer disease indexand the output of the prostate cancer medical image.Combine two different output values, that is, combinethe prostate cancer disease index and the prostate cancermedical imaging index to adjust the weight. Because theclinical diagnosis of prostate cancer mainly relies onserum PSA, when digital rectal examination through rectalultrasound and pelvic MRI are performed through digitalrectal examination, CT is less sensitive to the diagnosis

of early prostate cancer. Therefore, the weight of theimage in the early diagnosis of prostate cancer should beless than that in the later stage. This requires a corre-sponding algorithm to combine the two, and finally drawa DPC system output. Then according to the final outputvalue (DPC), the cancer stage is assisted in diagnosis.According to the Tumor Node Metastasis (TNM) guide-lines, prostate cancer can be divided into four stages (I,II, III, and IV): prophase, early stage, middle stage, andlate stage. The clinical stage of prostate cancer can becomprehensively evaluated according to the final com-bined output value (DPC).

2.2. The Machine-Aided Diagnosis System for Prostate CancerInputs Medical Images for Preprocessing in the Context of BigData. The clinical manifestations of prostate tumors are theobvious enlargement of tumor and the diffusion of watermolecules in living tissues, the increase of cell density, thechange of the macromolecular protein content, and theincrease of cell density in malignant lesions of cancer cells,which leads to the increase of tumor plane area and darkercolor of medical image. Therefore, the article uses the lesionarea and its gray level of medical images as indicators to assistin the diagnosis.

In the context of big data. Machine-assisted diagnosisof prostate cancer is based on six different disease indica-tors (tumor markers): HB, PSMA, FPSA, RBC, PAP andTPSA, as well as PET and CT. These two vectors are thelesion area and the density of the lesion area generatedas input to the input layer. In terms of medical imagesof prostate cancer, we are not able to directly obtain theinput of two image indexes through PET-CT images.Therefore, this will require preliminary preprocessing ofmedical images.

First of all, in some medical images, there may besome color labeling, which is called the noise of the image.Therefore, we performed gray-scale processing of prostatecancer medical images to obtain a complete gray-scalecancer image:

PGray = PR ∗ α + PG ∗ β + PB ∗ γ: ð1Þ

PR, PG, and PB, respectively, correspond to the threecolors of the original RGB color pattern in the originalimage. According to the characteristics of prostate cancermedical images, the value range of parameter α is0.25~0.35, the value range of β is 0.55~0.65, and the valuerange of γ is 0.1~ 0.2.

Our ultimate goal is to get the focus area of the medicalimage of prostate cancer. And because of that, the medicalimage has a lot of noise; we want to remove the noise of thecancer image. The purpose of Gaussian blur is mainly toreduce the overall image noise. The purpose is to calculatethe image gradient and edge more accurately PGray :

h x, y, σð Þ = 12πσ2

exp −x2 + y2

2σ2

� �ð2Þ


where σ determines the Gaussian variance for the inputparameter in order to determine the degree of blurring ofthe cancer image.

Then, Gaussian smoothing was performed on the pros-tate cancer image to obtain the processed prostate cancerimage f ðx, yÞgðx, yÞ:

g x, yð Þ = h x, y, σð Þ ∗ f x, yð Þ ð3Þ

where ∗ represents convolution, which will be converted intoa two-dimensional template for the operation of convolutionon cancer images hðx, y, σÞ.

The gradient and amplitude of prostate cancer medicalimages were then calculated. Image gradient, the directionof the gradient is the direction where the function f ðx, yÞchanges fastest. When there is an edge in the image, theremust be a large gradient value. On the contrary, whenthere is a relatively smooth part in the image, the grayvalue changes little, and the corresponding gradient is alsosmall. In image processing, the mode of gradient isreferred to as gradient, and the image composed of imagegradient is called gradient image. The edge of the imagehas two properties, direction and magnitude. The pixelchanges gently along the edge direction and dramaticallyperpendicular to the edge direction. This change on theedge can be detected by the differential operator, usuallyusing the first or second derivatives to detect edge. Wecalculate the amplitude and direction of the gradient withthe first-order finite difference.

The gradient of the medical image of prostate cancer aftersmoothing and filtering can be used to calculate the partialderivatives of x and y with the first-order finite differenceapproximation of 2 × 2gðx, yÞf x′ðx, yÞf y′ðx, yÞ:

f x′ x, yð Þ ≈Gx =f x + 1, yð Þ − f x, yð Þ + f x + 1, y + 1ð Þ − f x, y + 1ð Þ

2,

f y′ x, yð Þ ≈Gy =f x, y + 1ð Þ − f x, yð Þ + f x + 1, y + 1ð Þ − f x + 1, yð Þ

2:

8>><>>:

ð4Þ

In a rectangular coordinate system, the relation betweencoordinates, amplitude, and azimuth is

M x, yð Þ =ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiGx x, yð Þ2 + Gy x, yð Þ2,

q

Q x, yð Þ = arctanGx x, yð ÞGy x, yð Þ

!:

8>>><>>>:

ð5Þ

For prostate medical image, there is a need to get thefocus area and need to get the edge image of the focus area,where Mðx, yÞ represents the amplitude, which reflects theedge intensity of the cancer image; Qðx, yÞ represents theazimuth, which reflects the direction of the gradient; there-fore, when Mðx, yÞ obtained the local maximum, its corre-sponding gradient direction Qðx, yÞ reflects the direction ofthe edge (the edge direction is perpendicular to the gradientdirection), as shown in the figure.

In order to obtain more accurate edge images of can-cer lesion areas, nonmaximal suppression of gradientamplitude is also required at last; that is, the larger thevalue in the current gradient amplitude matrix is, the cor-responding edge point of the cancer image is at this point,which can only indicate that the gradient value of thispoint is larger. In this way, the step of nonmaximum sup-pression is completed. A large part of nonedge points areremoved, and binary image output is finally obtained, asshown in the figure.

At this point, after detecting the edge of the prostatecancer lesion area, we will try to obtain one of the inputparameter areas. This system uses the eight-directionFreeman chain code to calculate the irregular area.

The reason why it is called eight-direction chain code isthat it starts from the starting pixel point and scans the nextadjacent pixel point, then looks at the relative position of theoriginal pixel point and the adjacent pixel point, encodesaccording to the eight-direction diagram, and scans succes-sively to obtain the Freeman chain code.

Then, carry out front vector and back vector annotationon the chain code:

s1 i½ � =−1, i > 0, pi × 1 i½ � ⋅ y > pi × i − 1½ � ⋅ y, i = 0, pi × 1 0½ � ⋅ y > pi × N − 1½ � ⋅ y,0, i > 0, pi × 1 i½ � ⋅ y = pi × i − 1½ � ⋅ y, i = 0, pi × 1 0½ � ⋅ y = pi × N − 1½ � ⋅ y,1, i > 0, pi × 1 i½ � ⋅ y < pi × i − 1½ � ⋅ y, i = 0, pi × 1 0½ � ⋅ y < pi × N − 1½ � ⋅ y,

8>><>>:

9>>=>>;

s2 i½ � =−1, i <N − 1, pi × 1 i + 1½ � ⋅ y > pi × i½ � ⋅ y, i =N − 1, pi × 1 N − 1½ � ⋅ y > pi × 0½ � ⋅ y,0, i <N − 1, pi × 1 i + 1½ � ⋅ y = pi × i½ � ⋅ y, i =N − 1, pi × 1 N − 1½ � ⋅ y = pi × 0½ � ⋅ y,1, i <N − 1, pi × 1 i + 1½ � ⋅ y < pi × i½ � ⋅ y, i =N − 1, pi × 1 N − 1½ � ⋅ y < pi × 0½ � ⋅ y:

8>><>>:

9>>=>>;

ð6Þ


Add the former vector annotation and the latter vectorannotation, and the result is s½i�:

s i½ � =−1,

0,

1,

8>><>>:

a = 1, 2, 3,

a = 0, 4,

a = 5, 6, 7:

ð7Þ

The final target area can be expressed as

PArea = 〠N−1

i=0pix1 i½ �x ∗ s i½ � +N1: ð8Þ

After converting the original image to grayscale image,calculate another parameter of the model input:

PGray‐level =1nl〠nl

i=1Vpi, ð9Þ

where nl is the total pixel point in the prostate cancerlesion area and is the pixel value corresponding to thepixel point Vpi.

In this module, we have two purposes: first, the modelneeds to screen the medical images of prostate cancerthrough two indicators; second, the need to finally obtainthe input and PAreaPGray‐level.

2.3. Input Model of the Machine-Aided Diagnosis System forProstate Cancer in the Context of Big Data. The input modelof the machine-aided diagnosis system of prostate cancer inthe context of big data needs to properly process all the dig-ital information obtained and convert irregular, unreason-able, and invalid information into qualified and effectivedigital information. In particular, such digital informationcannot be faulted, and it is more necessary to process irregu-lar and unreasonable invalid information into effective input,so as not to affect the operation of the system.

In the prostate cancer machine-assisted diagnosis systemin the context of big data, the input is divided into two parts.One part is the input of disease indicators into ACPðtÞ.Among these test results, tumor markers are the most valu-able signals for diagnosis, treatment, and prognosis. There-fore, the prostate disease index input consists of six diseaseindexes, namely, HB, PSMA, FPSA, RBC, PAP, and TPSA.The vector ACPðtÞ that combines these six disease indexesis the input to the system:

ACP tð Þ = XTPSA, XRBC, XHB, XFPSA, XPAP, XPSMA½ �: ð10Þ

Although the disease index of prostate cancer is animportant indicator to assist the diagnosis, it is obviouslytoo superficial and insufficient to simply rely on the diseaseindex as the judgment and decision. Therefore, based on tak-ing the disease index as the input of the system to make thediagnosis decision, the system also adds a part of the inputimage index input AHPðtÞ. AHPðtÞ is two kinds of medicalimage indexes, the lesion area and lesion area density, pro-

duced by PET-CT taken by patients. And these two indica-tors form a vector AHPðtÞ as another input to the systemPAreaPGray‐level:

ACP tð Þ = PArea, PGray‐level� �

: ð11Þ

The input model is not only the beginning of the theoret-ical machine-aided diagnosis system but also the beginningof the two input neural network models in the input layerof neural network.

2.4. Neural Network Model of Machine-Aided DiagnosisSystem for Prostate Cancer under Big Data Environment. Thispart is the study of prostate cancer by six different diseaseindexes (tumor markers), Hb, PSMA, FPSA, RBC, PAP,TPSA, and PET-CT of two kinds of medical image metrics,the lesion area PArea and lesion area density PGray‐level gener-ated by the two vectors as input, and then adjusted by weightto study the diagnosis process of prostate cancer by diseaseindex and medical image, respectively. Therefore, this stageof research should be divided into two parts: the first part isthe reasonable evaluation and allocation of the weight of dis-ease indicators and medical image indicators; the second partis the diagnosis process of the intelligent diagnosis system,and a preliminary diagnosis result is obtained by adjustingthe weight and bias of tumor markers.

2.4.1. Reasonable Evaluation and Allocation of Disease Indexand Image Index Weight of Prostate Cancer. In this study, theinpatient information of more than 8,000 prostate patients inthree hospitals in China was studied, and about 2,156,528data items were collected and statistically analyzed. There-fore, under such a large data volume, we can continuouslyadjust and evaluate the weight through experiments. Theneural network model of the machine-aided diagnosis systemof prostate cancer in the big data environment is a process ofreasonably adjusting the weight and deviation of each nodeof prostate cancer after input. According to the overall struc-ture of intelligent medical big data decision model diagnosissystem, the neural network model following the process ofthe diagnosis of prostate cancer is divided into two parts.On the left side is through early collection of statistics of alarge number of cases of prostate cancer as a model of train-ing data information; the goal is to continuously adjust theweights and bias between the various points. With six differ-ent disease indexes (tumor markers): Hb, PSMA, FPSA, RBC,PAP, and TPSA generated by the vector ACPðtÞ as input;intelligent diagnosis system is ultimately getting the returnerror of disease index to adjust the parameters of the intelli-gent diagnosis system. The right part is a large number ofprostate cancer case images collected and counted in theearly stage, and two medical image indexes obtained throughpreliminary preprocessing are used as data information ofmodel training, so as to constantly adjust the weight and biasbetween various points. And in patients with a type of PET-CT of two kinds of medical image indexes, the lesion areaand lesion area density, generated vector AHPðtÞ as input,intelligent diagnosis system is ultimately getting the returnerror of disease index of intelligent diagnosis system with


parameter adjustment PAreaPGray‐level. Therefore, the input ofthe two parts can be expressed by vectors as follows:

XTPSA, XRBC, XHB, XFPSA, XPAP, XPSMAf g = ACP tð Þ,XArea, XGray‐level� �

= AHP tð Þ:ð12Þ

When the ACPðtÞ and AHPðtÞ generated by six tumormarkers and two medical image indicators are inputted intothe intelligent diagnosis system, the weight between neuronsis adjusted to form the input of the next neuron, and eachneuron has a corresponding activation function, which isused to process the data.

The activation function selected is sigmoid function,which is a common S-shaped function in biology, also knownas the S-type growth curve. Because its value range is (0, 1),the input vector ACPðtÞ and AHPðtÞ of prostate cancer canbe mapped to the interval (0, 1). In the process of big datamedical-aided diagnosis of prostate cancer, there is no linearconnection between the upper and lower layers of the neuralnetwork, and the difference between these features iscomplex but not large. Therefore, sigmoid function is a suit-able choice for the model of big data-assisted diagnosis ofprostate cancer.

f = 11 + e−x

ð13Þ

In a multilayer network, the output of the previous layerwill serve as the input of the later layer:

am+1ðACPðtÞ,AHPðtÞÞ = f m+1ðWm+1am + bm+1Þ,

m = 0, 1,⋯⋯ ,M − 1: (16).M is the number of layers in the neural network. Six dis-

ease indicators and two medical image indicators can becalculated by weight to obtain a final cancer result, whichwe call the expected result (the desired result). Comparingthe expected result and the actual result (actual results), theerror function will reach the minimum value min E accord-ing to the gradient descent method. The purpose is to makethe diagnosis decision result more and more close to the realvalue by adjusting the weight, so that the weight and bias canreach a state of “perfect”. The error function of desired resultand actual results are

E ACP tð Þ,AHP tð Þð Þ w, bð Þ = 12〠n−1

k=0DDesired result −Dactual resultsð Þ2:

ð14Þ

Among them, the number of nodesnas the final output todiagnose prostate cancer under the large data of expectedresults with the real result would be a differentiation valueon prostate cancer; we eventually have to reach the goal toget the value to minimize the differentiation of prostate can-cer. When it reaches a certain small value is an optimal solu-tion, and finally, we, through the optimal solution to adjustthe weights and bias, optimize weights and bias:

w ACP tð Þ,AHP tð Þð Þ =w ACP tð Þ,AHP tð Þð Þ − η1∂E w, bð Þ

∂w ACP tð Þ,AHP tð Þð Þ=w ACP tð Þ,AHP tð Þð Þ − η1δx,

b ACP tð Þ,AHP tð Þð Þ = b ACP tð Þ,AHP tð Þð Þ − η2∂E w, bð Þ

∂b ACP tð Þ,AHP tð Þð Þ= b ACP tð Þ,AHP tð Þð Þ − η2δ,

ð15Þ

η is learning efficiency, and diagnostic accuracy is theessential requirements of the whole model. The higher theaccuracy is, the more perfect the intelligent diagnosis systemis. We adjust the weight; the diagnosis accuracy is the pur-pose of increase through six tumor markers and two medicalimage indexes which generatedACPðtÞandAHPðtÞtwoinputs; through the distribution of the weight, get the finaldiagnosis decision results. Therefore, it is crucial to adjustthe weights and bias to achieve a “perfect state”.

2.4.2. The Diagnosis Process of Prostate Cancer IntelligentDiagnosis System. In the first stage, we have obtained theweight and bias between each node by training with a largeamount of data. That is to say, the ownership weight of themodel of big data intelligent medical auxiliary diagnostic sys-tem has been determined. Therefore, at this stage, the processof the diagnosis of intelligent system will be analyzed. First ofall, the six different disease indexes (tumor markers): HB,PSMA, FPSA, RBC, PAP, TPSA, and PET-CT of two kindsof medical image metrics: two vectors ACPðtÞ and AHPðtÞgenerated by Area and Gray-level are used as inputs.

The next section will introduce the relationship betweendisease indicators and image indicators and how to adjustthe weight proportion between the comprehensive two diag-nosis results.

2.5. Decision-Making Model of Machine-Aided DiagnosisSystem for Prostate Cancer in the Context of Big Data

2.5.1. Decision Model of Expert System Based on RuleReasoning. From the previous step, the article has obtainedthe auxiliary diagnosis results derived from prostate diseaseindicators and medical imaging indicators. But the final deci-sion needs to combine the two to make a decision diagnosisof prostate cancer. First, you need to obtain a comprehensiveindex and then make a decision diagnosis, so that the deci-sion can be more accurate and reliable. Therefore, thissection introduces the relationship between the disease indexand the image index and how to adjust the weight ratiobetween the two comprehensive diagnosis results.

Among the decision models, the rule reasoning model is anew weight adjustment algorithm introduced by us, which isdeveloped from the rule-based expert system, a branch of theexpert system in the field of artificial intelligence. In thedecision-making model of the machine-aided diagnosissystem of prostate cancer in the context of big data, the articleuses a simpler expert system hair model based on rulereasoning.


The rule-based inference model is a knowledge setexpressed by rules, including the knowledge needed for rea-soning execution. As shown in Figure 2, in the process ofthe diagnosis of prostate cancer, low-level prostate cancerclinical symptoms are not particularly obvious, but in a highlevel of clinical symptoms of prostate cancer is obvious and inthe condition of prostate cancer research found that whenpatients clinical staging of prostate cancer in stage III or IVstage image characteristics than I or II when more obvious,therefore, if the step we have got prostate disease index andauxiliary diagnosis of medical image index to are still in thestage I or II, this may cause the index weight of the diseaseto be greater than the image index.

In the working memory of the rule-based inferencemodel, the complete data set required for inference executionwill be obtained. And the analysis results can be roughlyobtained by analyzing the changes of the index characteris-tics corresponding to different clinical stages as shown inTable 1.

Finally, it is the setting of an inference engine. When twodiagnostic results are taken as conditions, in order to com-plete the inference process, it is necessary to determine whichrule in the current cycle needs to be activated. In thedecision-making model of the machine-assisted prostate can-cer diagnosis system in the context of big data, each diagnosisis a definite value, and each set of conditions has a

Stage VIlater period

Stage IIImiddle period

Stage IIpreliminary period

Stage Iincipient period

Figure 2: The clinical staging marker image of prostate cancer.

Table 1: Reasonable distribution of weights corresponding to different staging results.

Case1 Case2 Case3 Case4

Case1 Weight (C1, C1) Weight (C1, C2) Weight (C1, C3) Weight (C1, C4)





corresponding data in the workspace. Therefore, in thedecision-making model of the machine-aided diagnosis sys-tem of prostate cancer in the context of big data, when con-ditions are determined, the corresponding rules can bedetermined, and usually there will be no rule conflict.

2.5.2. Staging Management and Treatment Suggestions ofProstate Cancer Machine-Assisted Diagnosis System underBig Data Environment. In the decision-making model ofthe machine-assisted prostate cancer diagnosis system inthe context of big data, get the final result through rule-based reasoning model DPC. Based on this result, performcancer staging and then cancer machine-assisted diagnosisstaging based on the final DPC. According to the tumor nodemetastasis (TNM) guidelines, prostate cancer is mainlydivided into four stages (I, II, III, and IV) in the early, middle,and late. Based on the final output value (DPC), the clinicalstaging of prostate cancer was comprehensively evaluated.Table 2 shows the staging threshold of the machine-assisteddiagnosis system for prostate cancer. At the same time,through the comprehensive evaluation of clinical stagingjudgment and phase estimation, the machine-aided systemcan recommend different treatment schemes to doctors, suchas active monitoring, drug therapy, resection, endocrine ther-apy, radiotherapy, and chemotherapy.

3. Experimental Performance

3.1. Experimental Data Collection, Classification, andPreprocessing

(1) Before the experiment, collect all the medical infor-mation needed from three first-class hospitals inChina: Xiangya Hospital, Xiangya Second Hospital,and Xiangya Third Hospital, and classify these infor-mation. Besides, the data center collects variousinformation items from different aspects to help thesystem-assisted diagnosis

(2) The team collects a large number of prostate cancermedical data and makes corresponding classificationstatistics and preprocessing and then does the corre-sponding recording work. Because of the largeamount of medical data collected, although it is med-ical data related to prostate cancer, only a part of themedical data is used in this system. Therefore, inorder to ensure the smooth progress of the experi-ment and its rationality and accuracy, we extractedmore than 8,000 prostate patients’ data from the datacollected by the three hospitals and extracted 16,143items of systematic structured and effective informa-tion from the data of these patients. Figure 3 showsthe classification items of medical data informationabout prostate cancer collected by the three hospitals

3.2. Initialize System Data Variables. (1) After testing andanalyzing more than 8,000 cases of prostate patients as atraining set, some parameters of the intelligent medical sys-tem are set as follows: initial weight determination; initialweight should be set to different values. Therefore, as for

the initialization of the weights, design a random occurrenceprogram to generate a set of 0~ 0.5 random numbers as theinitial weight of the network; as for the minimum trainingrate, the initial value can be set to 0.9 since the training ratewill be automatically adjusted; as for the dynamic coefficient,its selection is generally 0.6~ 0.8; the allowable error is0.001~0.00001. According to clinical medical standards,some standard medical indicators data are shown in Table 3.

(2) The intelligent system image index initializationinformation is preprocessed according to two medical imageindicators generated by PET-CT, and the area of the lesionarea and the lesion area are obtained. Gray-level two imageindicators, the resulting vectorACPðtÞ, as the initial inputto the intelligent system.

In this process, because prostate cancer images have a lotof noise, preexperiment experiments were carried outthrough a large number of images of prostate cancer beforethe experiment, and various parameters suitable for process-ing prostate cancer images were calculated and grayed out byfilters and double thresholds. The postprocessing of theimage is shown in Figure 1. The edge intensity of the lesionarea is compared with the surrounding edge intensity. Theresulting edge range will be more accurate and ensure thatthe final intelligent system correctly diagnoses the diagnosis.Figures 4 and 5 show the comparison of the intensity of edgepixels after detection with the intensity of surrounding pixels.It can be clearly seen that the edge intensity is much higherthan the surrounding pixel intensity. In Figure 5, the x-axisand y-axis is the length and width of the image, respectively,and the z-axis corresponds to the gray level of the grayscaleimage. The 3D effect shows that the gray scale of the prostatecancer lesion area is much higher than that of other regions.

The whole preprocessing process is to first perform gray-scale processing on the image and then calculate the gradientamplitude of the grayscale image, nonmaximum value sup-pression, and the threshold processing finally obtains thebinary image output and obtains a binary value suitable forcalculating the area of the lesion area. The image is calculatedby the Freeman chain code for the irregular connected graph,as shown in Figures 6 and 7:

After obtaining the two-part data index, the system usesthe data index generation vector as the initial input of the sys-tem model. Only accurate indicator data can make an accu-rate auxiliary diagnosis.

(3) In the big data environment of more than 8,000patients with prostate cancer, the intelligent medical systemtrained the data to get the corresponding weight. The intelli-gent system has a specific numerical division of the four clin-ical stages of prostate cancer. In terms of medical diseaseindicators, the smart medical system is through six prostatecancer disease indicators and two medical image indicatorsby patients were used for assisted diagnosis. Therefore, in

Table 2: The staging threshold of the machine-assisted diagnosissystem for prostate cancer.

Clinical stage Stages I Stages II Stages III Stages IV

Threshold (DPC) (0, 0.25] (0.25, 0.50] (050, 0.75] (0.75, 1.00]


the two modules of prostate cancer disease indicators andmedical image indicators, according to the weight of eachdisease indicator and medical image indicators plus the train-ing result data, the system will give the weight between everytwo nodes reasonable. The weight distribution of prostatecancer disease indicators and image indicators is shown inFigure 8.

(4) In the big data intelligent medical system decisionmodel, in order to accurately determine the clinical stage ofprostate cancer, the system combines the disease index andthe medical image index to assist the diagnosis. However,prostate cancer often occurs in the posterior lobe, and thereare no obvious symptoms in the early stage. Patients withprostate cancer often have symptoms such as frequent urina-tion, difficulty in urinating, thinning of urine, prolonged uri-

nary tract, dysuria, and urinary retention. Prostatichyperplasia is the same, so it is difficult to diagnose prostatecancer based on this, mainly relying on digital rectal exami-nation to make judgments. Therefore, in the rule-based rea-soning algorithm, set the rules to the early, middle, and latethree weights. Because medical images are not very obviousin early prostate cancer, if the prostate cancer disease indexmodel and the image index model result are in the early stage,the image model will occupy a smaller proportion. If theprostate cancer index model and the image index modelresults are at a later stage, the proportion of medical imageindicators is gradually increasing. Table 4 shows the rationaldistribution of the corresponding weight Wdisease indicator ofthe disease indicator. The corresponding medical image indi-cator is expressed as W image indicator:

W image indicator = 1 −Wdisease indicator: ð16Þ

As shown in Table 4, since medical imaging has no obvi-ous characteristics in early prostate cancer, the reasonableallocation weights of disease indexes and imaging indexesare divided into 4 cases to set the weights reasonably. (a)When both the prostate disease index and the medical imag-ing index point to stages I and II, the weight of the diseaseindex is set to be slightly larger than the weight of the medicalimaging index. (b) When both the prostate disease index andthe medical imaging index point to the third and fourthperiods, the weight of the disease index is set to be slightlysmaller than the weight of the medical imaging index. The

168

279

97

286

112

529

314

18

948

2,429

39

9

0 500 1,000 1,500 2,000 2,500 3,000

Patient information

Outpatient service

Doctors’ device in outpatient

Be hospitalized

Diagnosis

Electronic medical records

Doctors’ device in clinical

Inspection records

Medical laboratory records

Routine inspection records

Operation records

Drug records

X 10000Data item

Figure 3: The classification of items for collecting medical data for prostate cancer patients.

Table 3: The standard indicator data for normal people.

Indicator Normal range

Total prostate-specific antigen (TPSA) 0-4.0 ng/ml

Free prostate-specific antigen (FPSA) 4-20 μg/L

Hemoglobin (HB) 120~165 g/LRed blood cell (RBC) 12~15 g/100ml

Acid phosphatase (PAP) 0~3.5 ng/ml

Prostate-specific membrane antigen (PSMA) 0~4 ng/ml

Area of the lesion-area <15mm

Gray-level of the prostate <180


other two situations are somewhat special. The clinical stageof the disease index model and the medical image model arecompletely different. Because in the early stages of prostatecancer, medical image detection is indeed very convincing.Therefore, in order to ensure accuracy and rationality, ourweight design has been processed as follows. (c) When themedical image index points to stages I and II and the prostatedisease index points to stages III and IV, the weight of thedisease index is set to be much larger than the weight of theimage index. (d) When the medical image index points tostages III and IV, and the prostate disease index points tostages I and II, the weight of the disease index is set to bemuch smaller than the weight of the image index.

3.3. Data Evaluation and Analysis of Prostate Cancer in theBig Data Environment. TPSA is a specific marker of prostatecancer, and it is also meaningful for the diagnosis of prostatecancer with no obvious symptoms in the early stage. Asshown in Figure 9, in the five years from 2011 to 2015, the

average value of TPSA from 2011 to 2013 increased from18.63 ng/ml to 45.2 ng/ml, reaching its peak in 2013, butdeclined from 2013 to 2015. The trend in the past five yearsindicates that the condition of prostate patients is effectivelycontrolled. It is generally believed that TPSA is less than4ng/ml normal value, and TPSA is greater than 10ng/ml,which indicates an increased risk for prostate cancer patients.When the average level exceeds 50 ng/ml, the patient is likelyto have prostate cancer.

FSPA/TSPA, another important diagnostic indicator forthe big data intelligent assisted diagnostic system, is also animportant basis for assisted diagnosis and treatment. Asshown in Figure 10, the normal range of FPSA/TPSA isaround 0.25, but for the five years from 2011 to 2015, theFPSA/TPSA values are below 0.25 and are in a decline from2011 to 2014. The value of FPSA/TPSA has fallen to 0.05 in2014, and when FPSA/TPSA is below 0.1, the incidence ofcancer will reach a high level of 56%. This shows that mostpatients with prostate cancer have been deteriorating duringthis period time and are not well-controlled. In these struc-tured medical data, we can further realize that in these fiveyears, most of the patients in the three hospitals are in thestages III and IV of cancer.

So far, the causes of prostate cancer have been dividedinto two major modules, one of which is genetically leadingto prostate cancer. To prove this claim, we extracted datafrom all major prostate patients from 2011 to 2015 and madethe analysis for a dangerous proportion of data, setting thenumber of uninherited patients to 1, and the genetic patientsare the multiples of uninherited patients. From the tens ofthousands of data, we can roughly draw two conclusions.One is that the number of patients with genetic disease ismore than five times of the total number of patients, whichindicates that heredity is one of the major causes of prostatecancer; another point is that it can show from the trend ofdata that this multiple relationship is constantly risingbetween 2011 and 2015, and the proportion of genetic dis-eases is increasing at the same time as the number of patientsincreases. This shows that the number of genetic patients isincreasing year by year.

The other is diet that leads to prostate cancer. First, peo-ple who love smoking have a higher chance of developingcancer. Many people know that smoke contains a lot of dirtythings, and in the process of smoking, those substances willbe sucked into the body. Second, vitamin A is a trace elementrequired by the human body. Absorption of a certain amountof vitamin A can promote the healthy development of humanbody, and excessive amount will lead to a large increase insaturated fatty acids in the human body, thereby causinglesions in prostate cells. Third, carotene has a certain pro-moting effect on the pathogenesis of prostate cancer. Fourth,a high-fat diet is also a cause of prostate cancer because itcontains many animal fats and some fatty acids. If peopleeat more of these things, some of the meat in the body isdigested into different types of fatty acids; so in the longrun, the chance of developing prostate cancer will be greatlyimproved. So this is why many doctors say that patientswho have cancer have to eat less meat, because eating moremeat will hurt their body’s immune system.

200

150

100

Der

ivat

ive

50

0

250 200 150 100 50y x0 300

20010050 200 150100

Figure 4: Three-dimensional image of pixel intensity in prostatecancer lesions.

100

200

300

400

500

600

700

800

900

100 200 300 400 500 600 700 800 900

y

x

Figure 5: Plane display of pixel intensity in prostate cancer lesions.


The incidence and mortality of prostate cancer are sec-ond only to lung cancer and ranks second in cancer mortal-ity. As shown in Figure 11, the first-stage cure rate ofprostate cancer is over 95%, the second-stage cure rate is over80%, the third-stage cure rate is less than 40%, and thefourth-stage cure rate is only 25%. The prostate cancer intel-ligent diagnosis system provides patients with corresponding

treatment recommendations based on the system diagnosisresults, as shown in Table 5:

True positive rate = true positivetrue positive + false negative

,

False positive rate =false positive

false positive + true negative:

ð17Þ

The prostate cancer machine-assisted diagnosis systemproved feasible in the experiment. As shown in Table 6 andFigure 12, the receiver operating curve (ROC), the ROC isthetrue positive rate – false positive rate curve: thex-axis rep-resents true; positive rate, they-axis represents the false posi-tive rate. From Figure 12, we can see that the curve is veryclose to (0, 1) point [22–24]. Therefore, the classificationaccuracy of the prostate cancer machine-assisted diagnosissystem is still very high. We can compare this accuracy withthe expert doctor. The results are as follows:

The main role of the prostate cancer intelligent diagnosissystem is self-evident, mainly for the preliminary diagnosisfor prostate patients, and to collect statistical informationaccording to the data. During the past five years, the vastmajority of the more than 8,000 patients in the three hospi-tals have gradually recovered under the treatment of doctors.In this experiment, the patient enters the hospital for diagno-sis, and the doctor will generate a diagnosis book for the firstdiagnosis. Our data are from three famous Xiangya hospitalsof Central South University. The accuracy of the diagnosis isdefined as the accuracy of the first patient diagnosis, not theaccuracy of the patient after multiple diagnoses. Figure 13shows the comparison of the diagnostic accuracy betweendoctors and intelligent systems. The blue bar represents thedoctor’s diagnostic accuracy, and the green bar represents

Figure 6: Comparison of grayscale image and gradient amplitude image.

Figure 7: Nonmaximum suppression image and binary image output.

Total prostate specific antigenProstatic acid phosphataseProstate membrane antigenFree prostate specific antigenHemoglobinRed blood cellArea of the lesion-areaGray-level of the prostate

32%

32%

35%

9%

68%3%

11%

10%

Figure 8: Weight distribution of various indicators of prostatecancer.


the machine-assisted diagnostic accuracy. When the numberof cases is relatively low, it is obvious that the diagnosis accu-racy of artificial experts is much higher than that of intelli-gent systems. However, as the number of cases increases,the diagnostic accuracy of artificial experts declines, whilethat of intelligent systems increases. According to the specificdata, when the number of cases increased from 200 to 8000,the accuracy of manual expert diagnosis decreased from97% to 81%, while the diagnostic accuracy of intelligent sys-tems increased from 61% to 87%. Therefore, it can be con-cluded that when the diagnostic data reaches a certainamount, the accuracy of the intelligent diagnosis system forprostate cancer will exceed that of the artificial expert. There-fore, the intelligent auxiliary diagnosis system can assist theartificial expert as an auxiliary diagnosis.

4. Discussion

In recent years, more and more people have proposed theconcept of intelligent medical system. Many medical systemshave implemented different methods and techniques and

involve many related technologies such as big data medicaltreatment and deep learning. Models for assisting prediction,diagnosis, and treatment have also become hot topics inmedical applications and intelligence. Next, the article willintroduce the research status related to our works in detail.

In the social discussion of the smart medical system,many people in the smart medical industry still hold an opti-mistic attitude and believe that the smart medical market isgreatly guided by policies. With the development of society,smart medical will inevitably become a major trend. Themedical reform is now divided into three stages: the first isdigitization, which is to digitize a lot of information includingmedical records, medical history, and information for detect-ing a certain disease; the second stage is the hospitalization ofmobile, this stage is more advanced than digital on the firstfloor, the change brought about is that the original patientand caregiver’s space inside the hospital is not limited; thethird stage is remoteization.

In fact, the three stages of medical intelligence are allrelated to the development of technology, and the reliabilityof technology makes medical institutions to trust enough.

Table 4: Weight distribution of combining disease indicators and image indicators.

Disease indicatorsStage I Stage II Stage III Stage IV

Image indicators

Stage I W image indicator<Wdisease indicator W image indicator<<Wdisease indicatorStage II

Stage III W image indicator>>Wdisease indicator W image indicator>Wdisease indicatorStage IV

18.63

26.72

45.2

35.36

20.17

0

5

10

15

20

25

30

35

40

45

50

2011 2012 2013 2014 2015

TPSA

(ng\

ml)

4Normal

Patients in three hospitals

Figure 9: The TPSA growth trend of the three hospitals from 2011 to 2015.


For example, electronic medical records are the developmentof smart terminals and networks in the hospital, while mobilehospitals rely on the progress of Wifi technology, andtelemedicine relies on the development of 3G and 4G net-works and mobile Internet and involves semiconductormanufacturing processes. Technology allows devices to workbetter with lower efficiency, such as the rapid transmission ofimage information after detection and the development ofthe Internet of Things, making the connections betweendevices and devices and people more smooth.

In recent years, the overall development of smart medicalsystems has been slow. The main reason lies in two points.The first point is that big data processing does not have anoverall architecture. The second point is that the medical datamodel is messy and untargeted.

In the study of the article, the system will be able todirectly connect to the CDR data interface in the hospitaland batch process the medical big data. Among them, inter-nal operations related to premedical data processing, datacleaning, and other operator operations are involved. For

0.22

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0

0.05

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0.15

0.2

0.25

0.3

2011 2012 2013 2014 2015

FPSA

/TPS

A

0.25 Normal

Patients in three hospitals

Figure 10: The changes in FPSA/TPSA trends in the three hospitals from 2011 to 2015.

25%

40%

80%

95%

0 10 20 30 40 50 60 70 80 90 100

Stage IV

Stage III

Stage II

Stage I

Stage IV Stage III Stage II Stage ICure rate 25% 40% 80% 95%

Cure rate

(%)

Figure 11: The cure rate of four stages of prostate cancer.


medical big data processing architecture design, we will useHadoop and HDFS big data processing framework plat-forms, based on operator operations and SQL layer process-ing. In the process of concurrent processing, part of themodel processing can be performed in parallel, therebyreducing a lot of time costs.

4.1. Research on Cancer Based on Neural Network. At pres-ent, there are more and more researches on cancer diagnosisbased on neural network methods. However, the currentneural network-based decision-making methods are rela-tively simple, basically based on disease indicators or accord-ing to medical image indicators to do auxiliary diagnosis andlack of sufficient medical data information. The weight of thedecision-making stage is unreasonable. Georgia et al. pro-posed a convolutional neural network- (CNN-) based intelli-gent diagnosis system for breast cancer diagnosis and relatedprocessing, through the related image processing, withimages as input, diagnose breast cancer, and classify breastcancer histopathology images [25–27].

Besides, Zhang et al. proposed an artificial neural net-work for the selection and accurate diagnosis of miRNA bio-markers for colorectal cancer [28] and proposed an artificialneural network (ANN)model. The designed ANNmodel canaccurately classify sample data as cancerous or noncancer-ous. By using ANN, circulating miRNAs can be used as non-invasive, sensitive, and specific diagnostic markers. Li et al.[29] proposed an expert level for lung cancer detection andclassification using the method of deep convolutional neuralnetworks. A three-dimensional convolutional neural net-work (CNN) was designed to detect pulmonary nodulesand classify them as malignant or benign diseases accordingto pathology and laboratory-confirmed results.

4.2. Research on Cancer Based on Machine Learning. Diseasediagnosis tests based on deep learning models are also widelyused [30]. The DenseNet neural network model wasproposed to classify benign and malignant mammographyimages, improve the DenseNet neural network model, andinvent a new DenseNet-II neural network model. In

Wojciech et al., to predict clinical outcomes by analyzingtime-series CT images of patients with locally advancednon-small-cell lung cancer (NSCLC), a deep learning predic-tion of lung cancer treatment response to a series of medicalimages was proposed [31].

In addition, Jung-Hoon et al. proposed a new machinelearning method for early detection of hepatocellular carci-noma to help doctors solve clinical problems by combininggenetic algorithms, support vector machines and featureoptimization [32]. Geeitha et al. and Thangamani et al. pro-posed a support vector machine risk scoring system for ovar-ian cancer patients [33, 34]. By selecting miRNA sets,support vector machine (SVM) classifiers were constructedto analyze miRNA and clinical factors independentlyrelated to prediction, and a risk scoring system wasconstructed.

4.3. Research on Cancer Based on Fuzzy Reasoning. The diag-nosis and probabilistic decision-making problem based onnon-small-cell carcinoma (NSCLC) medical system has alsobecome a hot issue. Wu et al. divided the evolution processof disease diagnosis parameters by clinical research andmodel movement of non-small-cell carcinoma and thenestablished parameters and evaluated the selection stage ofnon-small-cell lung cancer [35]. Finally, combined with clin-ical data statistical analysis and probability, the system canprovide accurate, rapid clinical data analysis and decision-making recommendations. In another study, [36] proposeda clustering range model based on image recognition andverified the accuracy of treatment by establishing a predictivedecision algorithm.

Literature [37] introduced a new type of prostate cancerauxiliary diagnosis model based on fuzzy inference system,combined with statistical analysis of intelligent assisted diag-nosis system and big data medical data decision, and finallycombined with doctors’ diagnosis to provide prostate cancerpatients with disease diagnosis and the corresponding treat-ment plans. In addition, Liu et al. also proposed a breast can-cer detection model based on fuzzy reasoning technology,which can determine whether the tumor classification isbenign or malignant, and developed a two-layer, high-success rate classifier based on type-2 fuzzy reasoning thatcombines expert doctors’ opinions to classify tumors in theBI-RADS category as benign or malignant [38]. Amin et al.proposed a model that was also based on fuzzy reasoning[22]. However, this study was based on a fuzzy reasoningmodel based on the change of time grayscale combined withthe texture information of cervical images to classify patientswith the risk of cervical intraepithelial neoplasia.

Table 5: Treatment recommendations of four stages for prostate cancer.

Clinical stage Treatment advice

Stage I Pay attention to physical changes and observe carefully

Stage II Prostatectomy, radioactive implantation

Stage III Endocrine therapy, orchiectomy (nonsteroidal antiandrogen intermittent therapy)

Stage IV Chemotherapy (CTX, 5-FU, ADM, VLB, and PTX)

Table 6: Parameter-lists of true and predicted parameter formachine-assisted diagnosis of prostate cancer.

Actual value Total

Predictive valueTrue positive (TP) False positive (FP) P′False negative (FN) True negative (TN) N ′

Total P N


5. Conclusions

In this study, a machine-aided diagnosis system for prostatecancer based on perceptron neural network and big datawas proposed to solve the problem of scarce medicalresources caused by the large population and underdevel-oped medical level in developing countries. The intelligentsystem takes the combination of six disease indicators andtwo medical image indicators as the input and continuouslyadjusts the weight and deviation of the neural network inthe context of medical big data to form an intelligent diagno-

sis model based on the neural network. The model allowsdoctors to diagnose prostate patients with greater accuracy.Although the intelligent system can provide diagnostic infor-mation and decision-making results, it can only serve as anauxiliary diagnosis decision-making system for doctors andcannot completely replace doctors. However, it can largelyreduce the work burden of doctors, improve the efficiencyof hospitals, and greatly improve the accuracy of doctors’diagnostic. Through the study of hospitalization informationof more than 8,000 prostate patients in three hospitals, about2,156,528 data items were collected and compiled for

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

True

pos

itive

rate

False positive rate

True positive rate

Figure 12: Receiver operating curve (ROC) for the classification rate of machine-assisted diagnosis of prostate cancer.

97% 97% 94% 91% 86% 81%

61% 66% 68% 79% 84% 87%

0

10

20

30

40

50

60

70

80

90

100

200 400 800 2000 5000 8000

DoctorSystem

(%)

Figure 13: Comparison of diagnostic accuracy between doctors and machine-assisted systems.


experiment. Experimental data shows that when the patientbase increases from 200 to 8,000, the accuracy of themachine-assisted diagnostic system will increase from 61%to 87%, and the doctor’s diagnostic accuracy will drop to81%, so the smart medical system can help doctors and med-ical experts make more effective treatments. From the study,it is concluded that when the patient base reaches a certainnumber, the diagnostic accuracy of the machine-assisteddiagnosis system will exceed the doctor’s expert.

In the future work, we will transfer the main work centersto big data and intelligent identification and constantly trynew algorithm research, such as SVM and decision tree algo-rithms, or deep learning algorithms, by comparing the prosand cons between them, continuously optimizing the systemmodel to continuously improve the accuracy of the diagnosis.

Abbreviations

PCA: Prostate cancerPET: Positron emission computed tomographyCT: Computed tomographyPAP: Prostatic acid phosphatasePSMA: Prostate membrane antigenHB: HemoglobinTPSA: Total prostate-specific antigenRBC: Red blood cellFPSA: Free prostate-specific antigenMRI: Magnetic resonance imagingTNM: Tumor node metastasisROC: Receiver operating curveNSCLC: Non-small cell lung cancer.

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request.

Disclosure

Jun Zhang, Zhihang Chen, Jia Wu, and Kanghuai Liu currentaddress is at Central South University, Changsha 410075,China.

Conflicts of Interest

The authors declare that they have no competing interests.

Authors’ Contributions

J.Z., Z.C., and J.W. conceived the idea of the paper. J.Z., Z.C.,J.W., and K.L. designed and performed the experiments;W.Z. and K.L. analyzed the data; Z.C. contributed reagents/-materials/analysis tools; J.Z. wrote and revised the paper. JunZhang, Zhihang Chen, JiaWu, and Kanghuai Liu contributedequally to this work.

Acknowledgments

This work was supported partially by “Mobile Health”Ministry of Education-China Mobile Joint Laboratory. This

research was funded by the Major Program of NationalNatural Science Foundation of China (grant number71633006) and the National Natural Science Foundation ofChina (grant number 61672540).

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