Effective machine learning-based skin disease diagnosis using PyTorch

Abstract

The application of machine learning in medical diagnosis has become a trend in research. Skin infection is one of the most seen diseases and one of the world’s most infectious diseases, influencing people of all ages. The reason for the explicit attention of the researchers in skin detection is due to the reason that skin disease is more visible compared to any other disease. In the past, varied methods have been proposed, which have rendered remarkable results. However, the presently functional models are trained on specific kinds of diseases and are limited to 4 to 5 classes, which is inefficient in detecting a large set of diseases. The paper offers a weightless model for detecting 23 different kinds of skin diseases. The model is trained on the PyTorch backend, which gives the flexibility of developing an algorithm. The model attained 96.37% accuracy on training data, and 87.75% accuracy on test data, which is expected to improve as the size of the dataset is increased.