Neural network classifier of hyperspectral images of skin pathologies

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 ba...

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Bibliographic Details
Published in:	Kompʹûternaâ optika Vol. 45; no. 6; pp. 879 - 886
Main Authors:	Vinokurov, V.O., Matveeva, I.A., Khristoforova, Y.A., Myakinin, O.O., Bratchenko, I.A., Bratchenko, L.A., Moryatov, A.A., Kozlov, S.G., Machikhin, A.S., Abdulhalim, I., Zakharov, V.P.
Format:	Journal Article
Language:	English
Published:	Samara National Research University 01-12-2021
Subjects:	basal cell carcinoma hemoglobin hyperspectral imaging melanin melanoma neural network classifier oncopathology vgg
Online Access:	Get full text
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Summary:	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 %.
ISSN:	0134-2452 2412-6179
DOI:	10.18287/2412-6179-CO-832