DEEP NETWORK-BASED METHOD AND SOFTWARE FOR SMALL SAMPLE BIOMEDICAL IMAGE GENERATION AND CLASSIFICATION

Abstract

Context. The authors of the article investigated the problem of generating and classifying breast cancer histological images. The widespread incidence of breast cancer explains the problem’s relevance. The automated diagnosing procedure saves time and eliminates the subjective aspect. The study’s findings can be applied to cancer CAD systems. Objective. The purpose of the study is to develop a deep neural network-based method and software tool for generating and classifying histological images in order to increase classification accuracy. Method. The method of histological image generation and classification was developed in the research study. This method employs CNN and GAN. To improve the classification accuracy, the initial image sample was expanded using GAN. Results. The computer research of the developed method of image generation and classification was conducted on the basis of the dataset located on the Zenodo platform. Light microscopy served as the basis for obtaining the image. The dataset contained three classes of G1, G2, and G3 breast cancer histological images. Based on the developed method, the accuracy of image classification was 96%. This is a higher classification accuracy compared to existing models such as AlexNet, LeNet5, and VGG16. The software module can be integrated into CAD. Conclusions. The developed method of generating and classifying images is the basis of the software module. The software module can be integrated into CAD.