Discussion on Deep-Learning Strategies for Diagnosis of Multiple Diseases in Fundus Diseases
Deep-learning algorithms can achieve the precise diagnosis of fundus diseases,which is crucial for the early diagnosis and timely intervention of these diseases.In recent years,various deep-learning diagnostic algorithms have been proposed to improve data augmentation,image enhancement,training strategies,and feature extraction.However,the systematic analysis of training strategies for the diagnosis of multiple fundus diseases is insufficient.Therefore,this study analyzes the performances of different strategies based on aspects such as data processing,training measurement,network model,and attention mechanism,thus providing a basis for the design of deep-learning algorithms to facilitate the diagnosis of various diseases.The results of independent training followed by comprehensive voting are superior to those of centralized training,although a higher training cost is incurred.On the training set,the performance differences among algorithms using centralized training are insignificant.However,on the validation set,ResNeSt50 performs the best and demonstrates its excellent generalization ability.The binary prediction results of the algorithms using single-disease independent training are significantly better than the comprehensive classification of multiple diseases,thus indicating that optimizing the classification network structure improves the classification results effect of category features.In the future,a specialized network structure that integrates clinical knowledge for specific detail enhancement shall be designed to strengthen the differences between interclass features,thus rendering the model more interpretable and improving the diagnostic accuracy.Such a multi-layered integrated network design might be one of the future directions for the diagnosis of ophthalmic diseases.
Deep Neural Network(DNN)voterattention mechanismdata augmentationmodel optimization
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