Early Detection of Diabetic Retinopathy Using Deep Learning

Diabetic retinopathy (DR) is a leading cause of blindness among diabetics, and early detection is essential to prevent irreversible vision loss. Automatic diagnosis of DR can be subjective and time-consuming, with varying image quality making accurate detection difficult. However, a global shortage of trained ophthalmologists, especially in underserved regions, limits access to timely and accurate diagnosis, further emphasizing the need for automated solutions. This study proposes a multi-stage deep learning-based solution for automated DR detection, severity classification, and lesion segmentation. The framework employs transformer-based architectures to capture both global retinal context and fine lesion details, ensuring accurate and reliable predictions. The model is trained and evaluated using publicly available high-quality datasets such as APTOS-2019, DDR, IDRiD, TJDR, and DIARETDB1, ensuring robustness and cross-dataset generalizability. The performance is assessed using accuracy and ordinal regression metrics for DR grading, while Dice coefficient and Intersection over Union (IoU) are employed for segmentation accuracy. This dual-resolution strategy and a comprehensive multitask deep learning framework ensure scalable, high-performance, and clinically relevant solutions.