AI-Powered Dermatology: Skin Disease Classification Using CNN

Most often, skin diseases affect millions around the world and hence require early and accurate diagnosis for appropriate treatment. Visual examination, biopsy, which can be time consuming and subjective, are traditional methods of dermatological diagnosis. The objective of this study is to determine the use of Convolutional Neural Networks (CNNs) for automated skin disease categorization in order to increase diagnostic accuracy and speed. On a diverse dataset, a ResNet50 based deep learning model was trained with a validation accuracy of 94.81%. The model was able to precisely classify the skin conditions such as melanoma, leprosy as well as eczema. The results show that CNN based models close the gap between dermatologists and primary care providers in rural and underserved areas. Even so, its strength is hindered by the dataset diversity, model interpretability, and ethical concerns of clinical deployment. Overall this study shows the possibility of using AI in dermatology for diagnosis of skin disease and the availability of a quality healthcare to the people.