Neural network classifier of hyperspectral images of skin pathologies

Vseslav O. Vinokurov, Irina A. Matveeva, Yulia A. Khristoforova, Oleg O. Myakinin, Ivan A. Bratchenko, Lyudmila A. Bratchenko, Alexander A. Moryatov, Sergey V. Kozlov, Alexander S. Machikhin, Ibrahim Abdulhalim, Valery P. Zakharov

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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 %.

Original languageEnglish
Pages (from-to)879-886
Number of pages8
JournalComputer Optics
Issue number6
StatePublished - 1 Nov 2021


  • Basal cell carcinoma
  • Hemoglobin
  • Hyperspectral imaging
  • Melanin
  • Melanoma
  • Neural network classifier
  • Oncopathology
  • VGG

ASJC Scopus subject areas

  • Engineering (miscellaneous)
  • Computer Vision and Pattern Recognition


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