Non-contact Skin Cancer Diagnostic System€¦ · Results and challenges running cloud based...
38
1 / 38 Results and challenges running cloud based diagnostics Dr.sc.ing. Dmitrijs Bļizņuks Riga Technical University Non-contact Skin Cancer Diagnostic System DOC Riga 2019
Transcript of Non-contact Skin Cancer Diagnostic System€¦ · Results and challenges running cloud based...
1 / 38
Results and challenges running cloud based
diagnostics
Dr.sc.ing. Dmitrijs Bļizņuks
Riga Technical University
Non-contact Skin Cancer
Diagnostic System
DOC Riga 2019
2 / 38
Outlines
• Skin cancer non-contact diagnostics
• Achievements
• Modular architecture: Pros & Cons
• Diagnostic algorithms
3 / 38
Why skin cancer?
4 / 38
Age-adjusted death rates for the 10 leading causes of death in United States
SOURCE: NCHS, National Vital Statistics System, Mortality.
5 / 38
6 / 38
7 / 38https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/melanoma-skin-cancer/survival
95%
5%Survival rates
8 / 38
Wavelengths’ skin penetration depth
9 / 38
Place your screenshot here
Cloud service
Handheld device
10 / 38
• More than 1500 lesions tested
totally
• More than 1.5 years of testing
in Latvian Oncology Clinics
• Tested by General Practice
doctor
• Found 2 melanomas during two
months
• Ongoing tests in Hungary and
Bulgary
11 / 38
10+ years of research in biophotonics
5 Dr.phys.
University of Latvia
Experienced doctors of the Oncology Center of Latvia
Dermato-oncologists
Hardware and software design3 Dr.Sc.ing.
Riga Technical University
12 / 38
✓ Non-invasive✓ Portable✓ Quantitative✓ Reliable (Sp=98%)✓ Uses various spectra✓ Image analysis within seconds✓ Wireless✓ Image evaluation in time (Archive)✓ Affordable
Main users
General Practice doctors
13 / 3813
14 / 3814
First page from
doctor’s view
15 / 3815
Acquired images
viewing and assigning
to the patient
16 / 3816
Patient control
17 / 3817
Patient’s skin
measurements
18 / 3818
Servers monitoring
and control
19 / 3819
Server’s load
monitoring
20 / 38
21 / 38
Cloud based diagnostics (SaaS)
Could be used as a standalone service for various image analysis
Calculation node 1
Checkyourskin.eu
Device 1Doctor 1Doctor 1
Patient 1Patient 1
Internet
Calculation
process 1-1
Calculation
process 1-m
Calculation node n
Calculation
process n-1
Calculation
process n-m
...
Patient nPatient n
Device nDoctor nDoctor n
Patient 1Patient 1
Patient nPatient n
22 / 38
23 / 38
24 / 38
Optical Density spectra of skin lesions
D.Jakovels et al. Application of principal component analysis to multispectral imaging data for evaluation of pigmented skin
lesions, Proc. SPIE 9032, Biophotonics—Riga 2013, 903204 (November 18, 2013)
25 / 38
Specific stair-like inner structure to keep light polarization
26 / 38
4 layer PCBs
5 separately controlled LED groups
4 LEDs in each group
up to 500mA to each diode
27 / 38
Raspberry Pi
Compute 3
module
USB WiFi
module
USB “IDS”
camera
USB 3G/4G
module
Central control module
Illumination
control (STM32)
LED current
drivers
Five groups of
4x LEDs each
USB
USB USB
SPI
LED supply voltage +0.3V
Current and voltage
control for each LED
Photodiode
Trigger
Mea
sure
d in
ten
sity
LED control board
LED ring
LED supply
voltage step-down
Analog
voltage
setpoint
Li-ion
battery
Power and
charging
Temperature
sensor
User buttonsDigital inputs
Analog
voltage
setpoint
Digital-to-
analog
convertor
SPI
Resistance
Modular architecture
• LED illumination,
• LED control,
• Camera,
• Central module,
• Wireless connectivity.
3G → 4G
WiFi 2.4GHz → 5GHz
Camera 1 Mpix / 5MPix
28 / 38
l=535nm
l=660nm
l=950nm
Nevus
p’ karte
Melanoma
p’ karte
29 / 38
Searching for marker
30 / 38
Stabilizing by marker
31 / 38
Removing hair
32 / 38
Searching for healthy skin
33 / 38
34 / 38
35 / 38
MELANOMA VS SEB.KERATOSIS
Melanoma
Seborejas keratoze
36 / 38
0.0
50.0
100.0
150.0
200.0
250.0
-2.0 0.0 2.0 4.0Me
an
AF
in
ten
sity fro
m s
ele
cte
d le
sio
n a
rea
, a
.u.
Mean p'max from selected lesion area (estimated from 100 maximal values of selected area), a.u.
Seborrheic Keratosis(n=13)
Hyperkeratosis (n=9)
Melanocytic Nevus(n=23)
Melanoma (n=3)
I. Lihacova, K. Bolochko, E. V. Plorina, M. Lange, A. Lihachev, D. Bliznuks, and A. Derjabo, "A method for skin malformation classification by combining
multispectral and skin autofluorescence imaging," SPIE Proc 10685, 1068535 (2018).
37 / 38
38 / 38
Acknowledgements
This work has been supported by European Regional Development Fund
project ‘Portable Device for Non-contact Early Diagnostics of Skin Cancer’
under grant agreement # 1.1.1.1/16/A/197
Dmitrijs Bļizņuks
