Research on classification diagnosis model of psoriasis based on deep residual network

Abstract Objective A classification and diagnosis model for psoriasis based on deep residual network is proposed in this paper. Which using deep learning technology to classify and diagnose psoriasis can help reduce the burden of doctors, simplify the diagnosis and treatment process, and improve the quality of diagnosis. Methods Firstly, data enhancement, image resizings, and TFRecord coding are used to preprocess the input of the model, and then a 34-layer deep residual network (ResNet-34) is constructed to extract the characteristics of psoriasis. Finally, we used the Adam algorithm as the optimizer to train ResNet-34, used cross-entropy as the loss function of ResNet-34 in this study to measure the accuracy of the model, and obtained an optimized ResNet-34 model for psoriasis diagnosis. Results The experimental results based on k-fold cross validation show that the proposed model is superior to other diagnostic methods in terms of recall rate, F1-score and ROC curve. Conclusion The ResNet-34 model can achieve accurate diagnosis of psoriasis, and provide technical support for data analysis and intelligent diagnosis and treatment of psoriasis.