Method and Software Tool for Generating Artificial Databases of Biomedical Images Based on Deep Neural Networks

TL;DR

This paper presents a new method and software tool that uses GANs to generate artificial biomedical images, aiding in training data creation and comparison with real datasets.

Contribution

The paper introduces a novel GAN-based architecture and a software system for generating and analyzing artificial biomedical images.

Findings

Developed a GAN architecture tailored for biomedical image synthesis

Created a software module for generating training datasets

Compared generated images with real biomedical image databases

Abstract

A wide variety of biomedical image data, as well as methods for generating training images using basic deep neural networks, were analyzed. Additionally, all platforms for creating images were analyzed, considering their characteristics. The article develops a method for generating artificial biomedical images based on GAN. GAN architecture has been developed for biomedical image synthesis. The data foundation and module for generating training images were designed and implemented in a software system. A comparison of the generated image database with known databases was made.