Intelligent detection and applied research on diabetic retinopathy based on the residual attention network

Abstract

AbstractThis study proposes a high‐accuracy (ACC) algorithm to automatically detect diabetic retinopathy (DR) and diabetic macular edema (DME) in retinal fundus images. Three DR datasets were obtained for use in this study: EyePACS, Messidor, and IDRid. In the EyePACS dataset, two DR classifications and five classifications experiments were conducted. The Messidor and IDRid dataset were graded DR and DME. After preprocessing, enhancement, and normalizing, common convolutional neural networks (CNN) were used to obtain the classification results. Afterward, an optimization method residual attention network (RAN) was introduced that was based on the residual attention module, and incorporated dilated convolution, so as to optimize the experimental results. The focal loss was then added to solve the imbalance problem. Next, a five‐fold cross‐validation strategy was introduced so as to assess and optimize the proposed model, after which the prediction ACC, sensitivity, specificity, area under receiver operating curve, and Kappa score were assessed. The proposed method RAN was shown to achieve 89.2% ACC (95% confidence interval [CI], 0.8782–0.9123) for two DR classifications (normal and abnormal) on the EyePACS dataset, 89.8% ACC (95% CI, 0.8751–0.9275) for two DR classifications on the Messidor dataset. The IDRid dataset achieved an ACC of 71.5% (95% CI, 0.6941–0.7423) for the two DR classifications. RAN mainly improves the results of commonly used CNN methods on the same dataset. Therefore, the classification and diagnosis of DR may be improved by adopting the proposed method.