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This paper describes the design and development of a Deep Learning-based system for Diabetic Retinopathy Detection. Diabetic Retinopathy pertains to the injury inflicted on the blood vessels located in the retina. The common primary signs are blurry vision, vision loss, and in some severe cases being completely blind. Damage to blood vessels in the retina of an eye leads to vision loss in a person suffering from Depends Diabetes. Diabetic Retinopathy can be classified into five stages which range from no retinopathy to proliferative DR. Early intervention is beneficial in lessening its severity. Therefore, the goal of the project is to create an algorithm that would assist in the detection and classification of diabetic retinopathy in retinal photographs. It employs modern machine learning such as EfficientNetB4 architecture, Contrast Limited Adaptive Histogram Equalization (CLAHE), and data augmentations using the Albumentations library. It is attained by ensuring that modularity is incorporated per the agile methodology and that comprehensive error management and extensive testing are conducted to ensure the robustness and reliability of the system. The system demonstrates prospective capabilities of the fully automatic medical image analysis systems and emphasizes the importance of interdisciplinary efforts in the creation of such systems.

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AI Systems Real-Time Focused Screening of Diabetic Retinopathy Using Deep Learning

  • 会议论文

  • 发表日期：

    2026年

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  • P. Nagaraju,
  • T. Srikar Sameer,
  • B. Narayana Shankar,
  • V. Surya Naveen,
  • Y. Syam Joseph,
  • S. Sai Sandeep

摘要

This paper describes the design and development of a Deep Learning-based system for Diabetic Retinopathy Detection. Diabetic Retinopathy pertains to the injury inflicted on the blood vessels located in the retina. The common primary signs are blurry vision, vision loss, and in some severe cases being completely blind. Damage to blood vessels in the retina of an eye leads to vision loss in a person suffering from Depends Diabetes. Diabetic Retinopathy can be classified into five stages which range from no retinopathy to proliferative DR. Early intervention is beneficial in lessening its severity. Therefore, the goal of the project is to create an algorithm that would assist in the detection and classification of diabetic retinopathy in retinal photographs. It employs modern machine learning such as EfficientNetB4 architecture, Contrast Limited Adaptive Histogram Equalization (CLAHE), and data augmentations using the Albumentations library. It is attained by ensuring that modularity is incorporated per the agile methodology and that comprehensive error management and extensive testing are conducted to ensure the robustness and reliability of the system. The system demonstrates prospective capabilities of the fully automatic medical image analysis systems and emphasizes the importance of interdisciplinary efforts in the creation of such systems.