Improved Skin Disease Classification Using Generative Adversarial Network

Identifying skin diseases, such as leprosy, Tinea Versicolor, and Vitiligo identification is one of the challenging tasks. Therefore, skin disease identification success rate is comparatively poor as compared to the other computer vision tasks. Traditional Deep Learning (DL) models are not successful in this domain due to the lack of a huge number of data. To address the problem, in the present work, we introduced a customized Generative Adversarial Network (GAN) to generate synthetic data. With data augmentation, we achieved maximum 94.25% recognition accuracy using DensenNet-121, which was 10.95% better than when no augmentation was employed. Source code is publicly available at https://github.com/DVLP-CMATERJU/SkinDiseases_GenerativeAI.git GitHub.