Diabetic Retinopathy Classification Using a Modified Xception Architecture

Diabetic retinopathy (DR) is one of the major causes of blindness worldwide. With proper treatment, early diagnosis of DR can prevent the progression of the disease. In this paper, we present a new feature extraction method using a modified Xception architecture for the diagnosis of DR disease. The proposed method is based on deep layer aggregation that combines multilevel features from different convolutional layers of Xception architecture. The extracted features are subsequently fed into a multi-layer perceptron (MLP) to be trained for DR severity classification. The performance of the proposed approach was assessed with four deep feature extractors, including Inception V3,MobileNet, and ResNet50 and original Xception architecture. Compared with typical Xception architecture, the aggregation of deep CNN layers can effectively fuse deep features and improve the learning process. Additionally, a transfer learning strategy and hyper-parameter tuning are adopted to further improve the overall classification performance. The performance of the proposed model was validated on the Kaggle APTOS 2019 contest dataset. Experiments demonstrate that the modified Xception deep feature extractor improves DR classification with a classification accuracy of 83.09% versus 79.59%, sensitivity of 88.24% versus 82.35% and specificity of 87.00% versus 86.32% when compared with the original Xception architecture.