Skin Disease Classification Using Convolutional Neural Networks

Abstract

Skin diseases are a threat to the health of the population of the world as approximately millions of people are affected. Timely and proper detection is important toward proper management and treatment. This paper discusses how Convolutional Neural Networks (CNNs) can be used in auto-recognition and auto-classification of a range of skin conditions. Based on the deep learning framework, we provided a powerful CNN model trained on the column of wide-spectrum dermoscopic images corresponding to various skin disorders. The model was benchmarked by the use of the data augmentation and transfer learning which lead to high scores in accuracy measures, i.e., precision, recall and F1-score. They also incorporated explainable AI techniques in order to guarantee clinical interpretability and trust. As experimentation indicates the traditional machine learning designs and the state-of-the-art methods cannot match our CNN model. Next steps will involve expansion of the dataset, inclusion of multi-modal data and real-time deployment in the clinic. The study emphasizes the revolutionary nature of deep learning in the context of dermatology, which will lead to better treatment of patients and outcomes.