A diabetic retina image classification method and system based on deep learning, belonging to the technical field of artificial intelligence. The method comprises: acquiring an eye fundus image; importing the same eye fundus image into a micro-hemangioma lesion recognition model, a bleeding lesion recognition model and an exudative lesion recognition model for recognition; and extracting lesion feature information according to a recognition result, and then using a trained SVM classifier to classify the extracted lesion feature information to obtain a classification result, wherein the micro-hemangioma lesion recognition model is obtained by means of extracting a micro-hemangioma lesion candidate region from the eye fundus image and then inputting same into a CNN model for training; and the bleeding lesion recognition model and the exudative lesion recognition model are respectively obtained by means of marking a bleeding lesion region and an exudative lesion region in the eye fundus image and then inputting same into an FCN model for training. The requirement for a network model description capability is reduced, so that a model is easy to train, and lesion focus regions can be located and sketched with regard to different lesions, thereby assisting a doctor in carrying out clinical screening.
DIABETIC RETINA IMAGE CLASSIFICATION METHOD AND SYSTEM BASED ON DEEP LEARNING