Robust Five-Class and binary Diabetic Retinopathy Classification Using Transfer Learning and Data Augmentation

• URL: http://arxiv.org/abs/2507.17121v1
• Date: Wed, 23 Jul 2025 01:52:27 GMT
• Title: Robust Five-Class and binary Diabetic Retinopathy Classification Using Transfer Learning and Data Augmentation
• Authors: Faisal Ahmed, Mohammad Alfrad Nobel Bhuiyan,
• Abstract summary: This paper presents a robust deep learning framework for both binary and five-class Diabetic retinopathy (DR) classification.<n>For binary classification, our proposed model achieves a state-of-the-art accuracy of 98.9%, with a precision of 98.6%, recall of 99.3%, F1-score of 98.9%, and an AUC of 99.4%.<n>In the more challenging five-class severity classification task, our model obtains a competitive accuracy of 84.6% and an AUC of 94.1%, outperforming several existing approaches.
• Score: 1.3492288506683114
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diabetic retinopathy (DR) is a leading cause of vision loss worldwide, and early diagnosis through automated retinal image analysis can significantly reduce the risk of blindness. This paper presents a robust deep learning framework for both binary and five-class DR classification, leveraging transfer learning and extensive data augmentation to address the challenges of class imbalance and limited training data. We evaluate a range of pretrained convolutional neural network architectures, including variants of ResNet and EfficientNet, on the APTOS 2019 dataset. For binary classification, our proposed model achieves a state-of-the-art accuracy of 98.9%, with a precision of 98.6%, recall of 99.3%, F1-score of 98.9%, and an AUC of 99.4%. In the more challenging five-class severity classification task, our model obtains a competitive accuracy of 84.6% and an AUC of 94.1%, outperforming several existing approaches. Our findings also demonstrate that EfficientNet-B0 and ResNet34 offer optimal trade-offs between accuracy and computational efficiency across both tasks. These results underscore the effectiveness of combining class-balanced augmentation with transfer learning for high-performance DR diagnosis. The proposed framework provides a scalable and accurate solution for DR screening, with potential for deployment in real-world clinical environments.

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