Mitigating Class Imbalance in Healthcare AI Image Classification: Evaluating the Efficacy of Existing Generative Adversarial Networks

This study investigates the adverse effects of class imbalance on classification models within healthcare, recognizing the critical importance of addressing this significant challenge in medical datasets and the essential role of precise diagnostic tools in the field. Class imbalance often occurs due to a lack of data for specific classes, such as rare diseases. Our research aims to determine the efficacy of advanced techniques like data augmentation, DCGAN, Pix2Pix, and diffusion methods in addressing the challenge of class imbalance in medical datasets. Using a subset of the HAM10000K dataset comprising 600 normal skin images and 600 melanoma images, the study gradually reduces the number of melanoma images to demonstrate the detrimental effects of class imbalance on classification accuracy. Additionally, the research provides insights into enhancing classification model performance in medical AI applications and challenges in the experimentation technique. The experiment results showcase promising enhancements in the classification model, particularly notable when employing Pix2Pix and Stable Diffusions. Each method exhibits distinct strengths and considerations, contributing to a comprehensive understanding of their efficacy. Future research endeavors should prioritize surmounting the challenges linked with Stable Diffusion. Moreover, exploring its potential for generating multi-modality medical images could unlock new avenues for enhancing classification model performance in imbalanced datasets.