Image-based skin disease detection: Psoriasis, Eczema, and Acne using VGG16 and ResNet-50

Millions of people around the world are affected by skin diseases, which represent a serious health risk. For these illnesses to be effectively treated, a timely and precise diagnosis is essential. This project uses deep learning techniques, specifically the VGG16 architecture and Resnet50 architecture, to provide a reliable solution for the categorization of skin diseases. The creation of an extremely precise and effective model for the automated classification of skin conditions is the main goal of this study. Three different classifications of skin conditions, including psoriasis, eczema, and acne, make up the dataset used in this project. The models are adaptable and capable of managing a variety of dermatological issues because each class in the dataset has been carefully chosen to reflect a broad range of skin disorders. The known convolution neural network (CNN) models called the VGG16 architecture and Resnet50 architecture are used because of their exceptional feature extraction capabilities. Move the pre-trained VGG16 model is refined using learning on the dataset of skin diseases. To guarantee the reliability of the models, a thorough cross-validation procedure is used for training, validation, and testing.