Research on Diagnosis of Dermatology Based on Deep Residual Neural Network

Abstract

Abstract The methods of dermatological clinical examination are mainly skin images, including dermoscopy. Residual neural network (ResNet) can predict diseases according to dermoscopy images and provide effective proposals for doctors. Based on the ResNet model, this article migrated the pre-trained model on ImageNet to simulation experiment, and used the Focal Loss function to solve the problem of experimental sample imbalance, including but not limited to operations such as flip, rotation, scaling, and loss function replacement, thereby improving network performance. The experimental results show that the model trained by our method can reach completely correct when it classified a small number of samples. Our model can reach accuracy rate of 90.08%, recall rate of 88.44%, and F1 score of 85.25%. Compared with the model with unmodified loss function at the same depth, our model has respectively improved by 1.3%, 4.62%, and 3.58% in the above three aspects, which indicates that our method is effective in predicting rare diseases, and in predicting common diseases the accuracy rate also achieves good results.