Research And Prototype Implementation Of Key Techniques For Image Assisted Diagnosis Of Fundus Lesions

With the rise of artificial intelligence and big data,assisted diagnosis technology based on deep learning is becoming more and more popular in the medical field.Computers assist doctors in diagnosis by interpreting medical images,which not only solves the problem of tension between doctors and patients,but also improves the accuracy of disease detection.Assisted diagnostic systems facilitate disease identification and reduce the risk of disease progression.Therefore,this thesis studies the key technologies of image-assisted diagnosis of fundus lesions.This thesis has completed the following work:(1)The image data enhancement method of fundus lesions is studied.Medical images are difficult to collect,unevenly distributed,and the size is not unique,This thesis uses the data enhancement strategy to process the data,and solves the problems of poor image quality and long tail distribution in the training set of classification model from the quality and quantity of the data set,and generates the processed high-quality retinal data set for the training of classification model.(2)Research a new fundus image classification and diagnosis model.Based on the research and analysis of existing deep learning-based classification and diagnosis algorithms,a weak supervised spatial enhanced classification model(WACN)based on attention mechanism is constructed.The model is an end-to-end weakly supervised training strategy,which can be used for multi-classification disease diagnosis.The performance of the model is better than that of the traditional diagnostic detection model.(3)Design a new super resolution algorithm.An iterative feedback network(SKIFN)based on selective convolution kernel attention mechanism is build on the basis of existing super-resolution algorithm research.The model allows input information to adaptively adjust the size of its receptive field on multiple scales,so as to improve the accuracy of target recognition.The model is evaluated quantitatively on the benchmark data set of SR,The performance of the model is better than the existing SR model.(4)Design and prototype implementation of assistant diagnosis system for fundus diseases.Combining the classification algorithm and super-resolution image generation algorithm of fundus disease diagnosis with the actual needs of medical field,a prototype of fundus disease image medical assistant diagnosis system(MDS)is designed and implemented.