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Neural network classifier of hyperspectral images of skin pathologies
V.O. Vinokurov 1, I.A. Matveeva 1, Y.A. Khristoforova 1, O.O. Myakinin 1, I.A. Bratchenko 1, L.A. Bratchenko 1, A.A. Moryatov 2, S.G. Kozlov 2, A.S. Machikhin 3, I. Abdulhalim 4, V.P. Zakharov 1

Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34,
Samara State Medical University, 443079, Russia, Samara, st. Chapaevskaya, 89,

Scientific and Technological Center for Unique Instrumentation of the Russian Academy of Sciences,
117342, Russia, Moscow, st. Butlerova, 15,

Ben Gurion University of the Negev, Israel, 8410501, Negev, P.O. Box 653 Beer-Sheva

 PDF, 1350 kB

DOI: 10.18287/2412-6179-CO-832

Pages: 879-886.

Full text of article: Russian language.

The paper presents results of using a neural network classifier to analyze images of malignant skin lesions obtained using a hyper-spectral camera. Using a three-block neural network of VGG architecture, we conducted the classification of a set of two-dimensional images of melanoma, papilloma and basal cell carcinoma, obtained in the range of 530 – 570 and 600 – 606 nm, characterized by the highest absorption of melanin and hemoglobin. The sufficiency of the inclusion in the training set of two-dimensional images of a limited spectral range is analyzed. The results obtained show significant prospects of using neural network algorithms for processing hyperspectral data for the classification of skin pathologies. With a relatively small set of training data used in the study, the classification accuracy for the three types of neoplasms was as high as 96 %.

hyperspectral imaging, neural network classifier, melanin, hemoglobin, oncopathology, melanoma, basal cell carcinoma, VGG.

Vinokurov VO, Matveeva IA, Khristoforova YA, Myakinin OO, Bratchenko IA, Bratchenko LA, Moryatov AA, Kozlov SG, Machikhin AS, Abdulhalim I, Zakharov VP. Neural network classifier of hyperspectral images of skin pathologies. Computer Optics 2021; 45(6): 879-886. DOI: 10.18287/2412-6179-CO-832.

The reported study was funded by the Russian Foundation for Basic Research under project 19-52-06005 MNTI_a.


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