Deep Convolutional Generative Adversarial Networks for Imbalance Medical Image Classification.

Abstract

Medical image classification is an essential task in clinical practice and research. It enables medical professionals to be assisted in diagnosing medical conditions accurately and efficiently, leading to improved patient outcomes and survival rates. However, traditional manual interpretation methods for diagnosing medical images have some drawbacks. Firstly, imbalanced medical images often exhibit a significant disparity in the number of samples across different classes, posing challenges in training accurate and robust models that can effectively learn from limited data in the minority class while avoiding biases towards the majority class. Secondly, the limited availability of labelled data will put a further load on the healthcare system, as labelling medical images is a time-consuming and resource-intensive task, often requiring expert knowledge. This paper proposed a generative adversarial network (GAN) with the purpose of improving the limitations associated with the imbalanced distribution of medical images. Based on the experiments conducted, it shows that the proposed model exhibits a high level of accuracy for two-class labelled dataset, with a low performance for the skin cancer dataset due to number of the labelled dataset is more than two