Task optimized vision transformer for diabetic retinopathy detection and classification in resource constrained early diagnosis settings

Diabetic Retinopathy (DR) is a progressive complication of diabetes and a leading cause of preventable blindness worldwide. Early detection and accurate classification of DR severity are critical for timely intervention but remain challenging, particularly in resource-constrained settings. While conventional deep learning (DL) models based on Convolutional Neural Networks (CNNs) have shown promising results, they often struggle to capture long-range dependencies in retinal fundus images and typically require substantial computational resources, limiting their utility on low-cost hardware. To address these challenges, this study introduces a Task-Optimized Vision Transformer (TOViT) model, specifically designed for DR detection and severity classification. The model integrates several optimization strategies, including layer-wise learning rate scheduling, attention head tuning, and embedding dimension refinement, to enhance feature extraction while maintaining computational efficiency. The model is further compressed through structured pruning and 8-bit quantization to support real-time deployment on Raspberry Pi-4 hardware. Evaluated on three large-scale public datasets, TOViT achieved a classification accuracy of 99%, with F1-scores exceeding 93% across all DR stages. Hardware implementation yielded real-time performance, processing at 8 frames per second with 120 ms latency, confirming its potential for use in portable, point-of-care screening devices. This work presents a scalable and clinically relevant approach for automated DR diagnosis, with promising implications for expanding access to early retinal screening in global healthcare systems.