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<p>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&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月7日

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  • Ramasubramanian Bhoopalan,
  • Priyadharshini Sekar,
  • N. Nagaprasad,
  • Tadesse Regassa Mamo,
  • Ramaswamy Krishnaraj

摘要

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.