Skin Disease Detection Using Deep Learning Techniques

Abstract

The effectiveness of deep learning methods in the identification of different skin illnesses is investigated in this article, with a focus on the VGG19 and Inception ResNetV2 frameworks. Leveraging the advanced features of VGG19 and Inception ResNetV2, the model is adept at processing intricate visual inputs, exhibiting particular strength in discerning subtle differences in texture, color, and form associated with diverse skin conditions such as dermatitis, eczema, psoriasis, nail fungus, and melanoma. The implementation of the deep learning architectures further enables the extraction of complex characteristics critical for accurate diagnosis. The model is trained on a wide range of datasets covering a wide range of skin conditions. Transfer learning greatly improves the model's performance, especially in situations where there are few labelled datasets. This innovative approach holds great promise in revolutionizing dermatological diagnostics, offering a precise and automated means of diagnosing skin illnesses. The potential for early identification and intervention stands to significantly improve patient outcomes in the field of dermatology.