VGG Convolutional Neural Network Classification of Hyperspectral Images of Skin Neoplasms

Boris V. Grechkin
Samara National Research University, Russia

Vseslav O. Vinikurov
Samara National Research University, Russia

Yulia A. Khristoforova
Samara National Research University, Russia

Irina A. Matveeva (Login required)
Samara National Research University, Russia


Paper #8964 received 28 Apr 2023; revised manuscript received 23 Sep 2022; accepted for publication 24 Sep 2023; published online 11 Nov 2023.

DOI: 10.18287/JBPE23.09.040304

Abstract

The article is devoted to the problem of early diagnosis of cancer. In last five years, various optical methods have been increasingly used to study biological tissues. This study aims to investigate the capability of a convolutional neural network classifier to diagnose skin cancers. The article analyzes hyperspectral images of malignant melanoma and pigmented nevus. A hyperspectral image classifier based on a deep learning neural network was developed. The results show a classification accuracy of diagnose prediction (on test data) at the level of 95%, which demonstrates the possibility of using machine learning for the classification of hyperspectral images of skin diseases. The results of the study can be applied in medical decision-making systems.

Keywords

hyperspectral imaging; malignant melanoma; classification; medical diagnostics; neural network; pigmented nevus; skin cancer

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