DR-IIXRN : Detection Algorithm of Diabetic Retinopathy Based on Deep Ensemble Learning and Attention Mechanism - Ai, Zhuang; Huang, Xuan; Fan, Yuan; Feng, Jing; Zeng, Fanxin; Lu, Yaping (Frontiers in Neuroinformatics, 2021)
Diabetic retinopathy (DR) is one of the common chronic complications of diabetes and the most common blinding eye disease. If not treated in time, it might lead to visual impairment and even blindness in severe cases. Therefore, this paper proposes an algorithm for detecting diabetic retinopathy based on deep ensemble learning and attention mechanism. Firstly, image samples were preprocessed and enhanced to obtain high quality image data. Secondly, in order to improve the adaptability and accuracy of detection algorithm, we constructed a holistic detection model DR-IIXRN, which consists of Inception V3, InceptionResNet V2, Xception, ResNeXt101 and NASNetLarge. For each base classifier, we modified the network model using transfer learning, fine-tuning and attention mechanisms to improve its ability to detect diabetic retinopathy. Finally, a weighted voting algorithm was used to determine which category (normal, mild, moderate, severe or proliferative diabetic retinopathy) the images belonged to. We also tuned the trained network model on the hospital data, and the real test samples in the hospital also confirmed the advantages of the algorithm in the detection of diabetic retina. Experiments show that compared with the traditional single network model detection algorithm, the auc, accuracy and recall rate of the proposed method are improved to 95%, 92% and 92% respectively, which proves the adaptability and correctness of the proposed method.