What question did this study set out to answer?

The aim is to assess the effectiveness of deep learning, particularly ResNet-50 architecture, in diagnosing and grading diabetic retinopathy.

May 14, 2026

Implementation of a Deep Learning-Based Approach for Diabetic Retinopathy Diagnosis and Severity Grading Using ResNet-50 Architecture

JVJyoti Tulshiram Vairal DWDr. M. A. Wakchaure

Key Points

The aim is to assess the effectiveness of deep learning, particularly ResNet-50 architecture, in diagnosing and grading diabetic retinopathy.
Utilized ResNet-50 and RSG-Net architectures for diagnosis and grading of DR.
Employed transfer learning and data augmentation methods to enhance model performance.
Analyzed multiple datasets, including the APTOS 2019 Blindness Detection dataset, for robustness.
Achieved improved grading accuracy across various DR stages from No DR to Proliferative DR.
Showed dependable performance in multi-class classification compared to binary methods.
Demonstrated potential for real-time screening in clinical settings.

Abstract

One of the most prevalent causes of blindness that can be prevented is diabetic retinopathy (DR). Because of this, automated screening tools that can identify early disease indicators in retinal fundus images are crucial. The severity levels of DR may now be correctly classified thanks to recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs). With an emphasis on the ResNet-50 and Retinopathy Severity Grading (RSG-Net) designs, this paper examines the most effective methods for identifying DR. The study demonstrates how DR grading may be made more dependable across several datasets, including the APTOS 2019 Blindness Detection dataset, by preprocessing photos, adding new data, and utilising transfer learning. Several stages of DR, from No DR to Proliferative DR, are examined to determine how well current CNN models can diagnose. The benefits and drawbacks of binary and multi-class classification methods, as well as their applicability in the clinic, are also thoroughly examined in this work. This study examines CNN-based techniques for early detection of DR and discusses how they can support widespread, real-time eye screening.

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