Research On Skin Disease Recognition Method Based On Deep Learning

In recent years,the use of computer-aided diagnostic techniques for the analysis and processing of medical-related images has been used more and more,especially for the auxiliary diagnosis of medical images related to skin diseases.The diagnosis process of skin diseases is complicated,and 'accurate diagnosis requires years of experience of doctors.Therefore,more accurate and effective image-assisted diagnosis methods can greatly help the timely diagnosis and treatment of skin diseases.Traditional dermatological disease-assisted diagnosis techniques require manual design of extraction features.It is often difficult to achieve diagnostic requirements by relying on these features for diagnosis.The use of deep convolutional neural networks can reduce manual intervention,allowing the recognition model to learn characteristics autonomously and improve recognition accuracy.This paper aims to study the classification and prediction methods of skin diseases based on deep learning,and optimize the classification model to a certain extent on the selected dataset to improve the classification ability of the model for skin diseases.The main work is summarized as follows:The first chapter mainly introduces the background and significance of using deep learning to assist in the diagnosis of skin diseases,and at the end of the chapter gives the main content and structure of this paper.The second chapter analyzes the framework of using deep learning to identify skin disease images,and establishes two skin disease datasets,which are dermoscopic image datasets and clinical image datasets,and introduces corresponding preprocessing methods,including images.Noise and image enhancement are two ways.The third chapter mainly analyzes the basic structure of the convolutional neural network and the related training foundation,and selects the relevant deep learning framework.Finally,the recognition effect of different convolutional neural network models is compared on the dermoscopic image dataset,and the base model is selected.The fourth chapter mainly introduces the improvement of the selected model in the previous chapter,including the activation function,the pooling method,and the improvement of the network structure.The experimental results show that the improved method adopted in this paper does have better recognition effect on the data set.The fifth chapter establishes a visual interface recognition system based on the trained skin disease recognition model.Then it is compared with medical related personnel to prove the research significance of using deep learning for auxiliary diagnosis.Chapter VI summarizes the research and gives corresponding conclusions.At the same time,it analyzes and discusses many problems in the research,and gives opinions and suggestions.