Abstract
Abstract. Since 2014, Artificial Intelligence (AI) image detection has developed rapidly in the medical field, while the global incidence of diabetes has increased rapidly. As an early sign of diabetic retinopathy (DR), the detection of diabetic microaneurysms is particularly important for the prevention and treatment of diabetes. Therefore, the artificial intelligence exhibition of DR microaneurysm screening based on fundus images shows great potential in this regard. This paper reviews the research progress of artificial intelligence technology for DR microangioma screening based on fundus images in recent years, and focuses on the improvement measures taken for different depth learning methods. These improvement measures aim to optimize model performance, improve training efficiency and enhance the generalization ability of the model. Some model experimental results are compared and analyzed in order to get the best model in a specific dataset or result. Finally, the current challenges are discussed and suggestions for the development of this field are put forward.
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