Early detection of skin diseases using deep learning approaches

Dermatological disorders are prevalent all over the world, and the rate affects people of all ages. The ability to diagnose skin diseases is important, especially when the disease is diagnosed early enough. In this work, we present a new deep learning model for skin disease classification that involves various methods of data enhancement and preparation to improve the quality of the images used in the analysis. Our data set was tested to compare the performance of competitive deep learning architectures such as ResNet50, VGG16, Inception V3, and EfficientNet. The proposed method for this work got an accuracy of 93% from a test data of 5000 images and is more efficient than several existing skin disease detection frameworks. We have observed high accuracy in our proposed skin disease prediction model using deep learning techniques.