Analyze Skin Disease using Xception Deep Learning Technique

Due to the low contrasts and visual similarities between different skin diseases, it can be hard to tell them apart, and correct diagnosis often requires medical training. However, computer programs that use computer vision and other forms of artificial intelligence can help dermatologists identify skin conditions. This study suggests an excellent way to recognize skin diseases by using Convolutional Neural Network (CNN) architectures, like the Xception model, to build an expert system that can quickly and correctly tell the difference between different types of skin diseases. To start, Kaggle Dermnet is a set of clinical images of skin diseases with 19,500 face pictures from 23 different categories. Transfer learning was used on the Dermnet dataset with models already taught to find more features. Three various skin disorders were collected using data from two different sources. Several performance evaluation indicators, the method's success was determined by precision, loss, accuracy, memory, and the F1 score. With transfer learning and reinforcement, the Xception model got a classification accuracy of 73.46 percent, which showed that the proposed method could work.