Automated diabetic retinopathy screening in resource-limited areas with attention-enhanced deep learning on fundus images - Binusha, Sornil A.; Sheeja, Herobin Rani C.; Rexiline, Sheeba I.; Renisha, G. (I-manager's Journal on Image Processing, 2024)
Diabetic retinopathy (DR) is a leading contributor to vision impairment, particularly in areas with limited resources where access to specialized care is scarce. This study introduces an automated screening system for DR using attention- enhanced deep learning on retinal fundus images, specifically designed for these regions. The system leverages convolutional neural network (CNN) technology with integrated attention mechanisms to focus on critical features indicative of DR, such as microaneurysms and hemorrhages, improving detection accuracy and reliability. Varied retinal fundus images were used for training and validation, with data augmentation applied to enhance model robustness. The model was optimized for deployment on low-cost hardware, ensuring feasibility in resource-limited settings. Performance evaluation demonstrated high sensitivity and specificity, and attention maps provided interpretability for healthcare providers. This automated system has the potential to enhance early detection of diabetic retinopathy (DR) in underserved areas, facilitating timely intervention and reducing the risk of blindness. By making advanced diagnostic tools accessible, this approach promotes equitable healthcare and helps to prevent vision loss globally.