Deep Learning for Diabetic Retinopathy Diagnosis: Challenges and Opportunities

The exceptional capability of the deep convolutional neural network lies in its proficiency in image feature extraction and learning, particularly in the domain of medical image processing [3]. The integration of deep learning and medical image field has significantly accelerated the advancement of intelligent medicine, and the employment of deep learning in diagnostic procedures is increasingly becoming a prevalent practice. The general framework for DR diagnosis encompasses data acquisition, medical image labeling, detection of lesion areas, lesion classification, and model assessment. Currently, diabetic retinopathy diagnosis heavily relies on global images for classification, which is prone to disruption from comparable samples, ultimately impacting the model's classification efficacy [4].