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Neural network classifier of hyperspectral images of skin pathologies
- Source :
- Компьютерная оптика, Vol 45, Iss 6, Pp 879-886 (2021)
- Publication Year :
- 2021
- Publisher :
- Samara National Research University, 2021.
-
Abstract
- 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 %.
Details
- Language :
- English, Russian
- ISSN :
- 24126179 and 01342452
- Volume :
- 45
- Issue :
- 6
- Database :
- Directory of Open Access Journals
- Journal :
- Компьютерная оптика
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.56328aba10164cac9d2f5ba883f36f77
- Document Type :
- article
- Full Text :
- https://doi.org/10.18287/2412-6179-CO-832