Enhancing diabetic retinopathy classification accuracy through dual-attention mechanism in deep learning

Abstract

Automatic classification of Diabetic Retinopathy (DR) can assist ophthalmologists in devising personalised treatment. However, imbalanced data distribution in the dataset becomes a bottleneck in the generalisation of deep learning models trained for DR classification. In this work, we combine global attention block (GAB) and category attention block (CAB) into the deep learning model, thus effectively overcoming the imbalanced data distribution problem in DR classification. Our proposed approach is based on an attention-based deep learning model employing three pre-trained networks, namely, MobileNetV3-small, Efficientnet-b0, and DenseNet-169 as the backbone architecture. We evaluate the proposed method on two publicly available datasets of retinal fundoscopy images for DR. Experimental results show that on the APTOS dataset, the DenseNet-169 yielded 83.20% mean accuracy, followed by MobileNetV3-small and EfficientNet-b0, which yielded 82% and 80% accuracies, respectively. On the EYEPACS dataset, the EfficientNet-b0 yielded a mean accuracy of 80%, while the DenseNet-169 and MobileNetV3-small yielded 75.43% and 76.68% accuracies, respectively. In addition, we also compute an F1-score of 82.0%, a precision of 82.1%, a sensitivity of 83.0%, a specificity of 95.5%, and a kappa score of 88.2% for the experiments. The proposed approach achieves competitive performance that is at par with recently reported works on DR classification.