An attention enriched encoder–decoder architecture with CLSTM and RES unit for segmenting exudate in retinal images

Diabetic retinopathy, an eye complication that causes retinal damage, can impair the vision and even result in blindness, if not treated on time. Regular eye screening is essential for patients with diabetics because diabetic retinopathy advances significantly without symptoms. Exudates are a primary symptom of diabetic retinopathy, and their automatic recognition can help in early diagnosis. The convolution operation which concentrates mostly on extracting the local features provides less emphasis on global information resulting the long-range dependencies to be addressed while traversing through multiple layers. The proposed segmentation model utilizes both the channel and spatial attention mechanisms to effectively establish the long-range dependencies at various levels of feature extraction. The proposed methodology also utilizes the convolutional long- and short-term memory algorithm during the propagation from input-to-state and from the state-to-state to take into account the spatiotemporal dependencies and the residual extended skip block for widening the network's receptive zone. Implementing the potentials of neural networks, this study excels at identifying complex patterns and minute features in retinal images. The effectiveness of the proposed method has been verified by conducting experiments on various retinal image datasets, such as IDRiD, MESSIDOR, DIARETDB0, and DIARETDB1, which clearly indicates the superiority of this method over other existing methods across a wide range of evaluation metrics, namely specificity, F1-score, accuracy, sensitivity, and intersection-over-union. Additionally, the model's ability to achieve an overall accuracy of 97.7% makes it a viable application that can provide clinicians important insights into the diagnosis and treatment of diabetic retinopathy.