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Volume 18, Issue 3
  • ISSN: 2666-2558
  • E-ISSN: 2666-2566

Abstract

Background

Diabetic Retinopathy (DR), a significant cause of vision loss globally, is characterized by retinal damage caused by diabetes. Early detection is vital to prevent irreversible blindness, yet challenges remain in accurately identifying DR stages and enhancing blood vessel visibility in fundus images. This paper aims to develop an early detection methodology for DR, addressing the need for early diagnosis and the difficulties in distinguishing DR severity through fundus imaging. The challenges in the early detection of Diabetic Retinopathy (DR) and enhancing blood vessel visibility in fundus images are multifaceted, including issues such as data imbalance, image noise, and complex patterns. By addressing these challenges through advanced ML techniques and image processing methodologies, the proposed methodology in the paper aims to overcome the limitations in early detection and severity assessment of DR, contributing to improved patient outcomes and vision preservation.

Methods

This study utilizes Machine Learning (ML) to analyze complex patterns in fundus color images of DR, employing spatial domain filtering to reduce image noise and address data imbalances across DR severity levels through data augmentation. A Convolutional Neural Network (CNN), enhanced with a Gabor filter, is applied for stage-specific DR detection and to pinpoint infected areas. The dataset includes 1000 color fundus images, with a 70:30 split for training and testing, respectively. The adoption of a Gabor filtering technique aims to refine the model’s performance further.

Results

The incorporation of a CNN with a Gabor filter has shown outstanding efficacy in detecting DR from fundus color images, achieving a training accuracy of 97.5%, validation accuracy of 96.5%, Cohen kappa score of 89.76%, and testing accuracy of 95.87%. This method effectively illustrates the disease-affected areas in the fundus images provided.

Conclusion

Through a comparative analysis of image processing techniques, this research highlights the advantages of using advanced DR analysis for image preprocessing. The proposed CNN-Gabor filter approach demonstrates significant success in identifying diabetic retinopathy in fundus color photographs and accurately delineating the affected regions, contributing valuable insights to the field of medical image processing.

© 2025 Bentham Science Publishers
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/content/journals/rascs/10.2174/0126662558293742240925044450
2024-10-16
2025-04-10

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/content/journals/rascs/10.2174/0126662558293742240925044450
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CNN-based Integrated Framework for Enhanced Diabetic Retinopathy Detection
Recent Advances in Computer Science and Communications 18, E26662558293742 (2025); https://doi.org/10.2174/0126662558293742240925044450
/content/journals/rascs/10.2174/0126662558293742240925044450
/content/journals/rascs/10.2174/0126662558293742240925044450
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  • Article Type:     Research Article
Keyword(s): convolutional neural network (CNN); deep learning (DL); Diabetic retinopathy (DR); integrated framework; machine learning (ML); retinal damage

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