Diabetic Retinopathy Prediction using YOLOv8

Abstract

Diabetic retinopathy (DR), a leading cause of blindness in diabetic patients, is traditionally diagnosed through the manual examination of retinal images. This process is resource-intensive and susceptible to human error. In response, this research introduces an innovative approach for the early detection of DR using YOLOv8, a state-of-the-art deep learning model renowned for its real-time object detection capabilities. By automating the detection process, our system focuses on identifying critical DR features, such as microaneurysms, hemorrhages, and exudates, which are indicators of disease progression. The methodology begins with thorough preprocessing of retinal images, including resizing, normalization, and contrast enhancement, to ensure high-quality inputs for model training. YOLOv8 is then trained to accurately locate and classify these DR features, demonstrating high performance across various stages of the disease. Key evaluation metrics, such as mean Average Precision (mAP), precision, recall, and F1-score, validate the model’s efficiency and accuracy, making it suitable for deployment in real-world clinical settings. Beyond its clinical applications, this study explores the potential integration of YOLOv8 into large-scale screening programs, particularly in underserved regions with limited access to specialized eye care. Future directions include enhancing the model’s generalizability, exploring ensemble learning techniques for improved accuracy, and investigating its potential for detecting other retinal diseases. This research contributes to the broader field of AI-driven healthcare, aiming to improve early diagnosis, reduce diagnostic costs, and support preventive care in the management of diabetic retinopathy. [2]

Introduction

With rising global diabetes rates, diabetic retinopathy (DR) has become a major cause of vision impairment and blindness, especially in low-resource regions. Early detection is essential but difficult due to the limited availability of specialists and the time-consuming nature of traditional manual diagnosis.

This project proposes an automated DR detection system using YOLOv8, a powerful real-time deep learning model, to identify critical retinal lesions (microaneurysms, hemorrhages, and exudates) from retinal images. The goal is to provide fast, accurate, and scalable diagnosis, enhancing accessibility and efficiency in DR screening programs, especially in underserved areas.

Key Objectives:

1. Early and Accurate Detection: Detect DR in its early stages to prevent vision loss.

2. Automated Classification: Use YOLOv8 to classify DR into different severity stages.

3. Real-Time Processing: Enable rapid diagnosis for high-volume screening.

4. Scalability: Deploy in rural, urban, and mobile health settings.

5. Reduce Human Error: Provide consistent, objective, and reliable results.

Literature Insights:

Various recent studies have explored AI-based DR detection:

• CNN-based models for image classification.

• Hybrid models using CNNs and Transformers for lesion localization.

• EfficientNet for resource-efficient classification.

• Emphasis on improving accuracy and deploying in real-world clinical settings.

Problem Scope:

Manual DR screening is resource-heavy and error-prone, especially in areas lacking trained ophthalmologists. This system addresses those limitations by:

• Automating detection and classification.

• Reducing diagnostic time and errors.

• Supporting large-scale screening programs.

Expected Outcomes:

• High Detection Accuracy of DR lesions.

• Efficient Diagnosis: Reduced time for analyzing retinal images.

• Real-Time Capability: Suitable for mass screening.

• Lower Human Error: Improved consistency and reliability.

• Wider Accessibility: Usable in diverse healthcare environments.

• Improved Patient Outcomes: Enables early treatment and vision preservation.

• Demonstrated Superiority: YOLOv8 outperforms models like EfficientNet and traditional CNNs in speed and accuracy.

Results:

The system demonstrated:

• Accurate lesion detection and classification.

• Real-time image processing.

• Reduced manual workload and error.

• Telegram integration for report delivery.

• Potential for integration into both fixed and mobile screening units.

Conclusion

The Diabetic Retinopathy Detection Using YOLOv8 project aims to significantly improve the early detection and diagnosis of diabetic retinopathy, a leading cause of blindness among diabetic patients. By leveraging advanced deep learning techniques, particularly the YOLOv8 model, this project has the potential to revolutionize how diabetic retinopathy is detected and classified in retinal images, offering a more efficient, scalable, and accurate solution compared to traditional manual screening methods. The expected outcomes of this project include early and precise detection of critical lesions, such as microaneurysms, hemorrhages, and exudates, which are indicative of diabetic retinopathy. This early detection will enable timely interventions, preventing the progression of the disease and preserving vision. Additionally, the use of YOLOv8\'s real-time processing capabilities will improve diagnostic efficiency, allowing healthcare providers to handle large volumes of retinal images swiftly and accurately. This makes the system especially beneficial in large-scale screenings and in underserved regions with limited access to specialized care. By automating the detection process, the system will reduce the reliance on manual image analysis, minimizing human error, and ensuring consistent, reproducible results. The accessibility and scalability of the solution will allow for its deployment in both urban healthcare facilities and remote or mobile healthcare settings, thus increasing the availability of early screening for diabetic retinopathy to a wider population. Ultimately, this project contributes to improving patient outcomes by facilitating early diagnosis and treatment, which can significantly reduce the risk of vision loss in diabetic patients. In conclusion, the Diabetic Retinopathy Detection Using YOLOv8 project represents a significant step forward in the integration of AI and deep learning into healthcare. Its successful implementation will not only enhance diagnostic accuracy and efficiency but also support the broader goal of providing more equitable healthcare access. By addressing the challenges associated with diagnosing diabetic retinopathy, this system has the potential to improve global health outcomes, particularly in regions with limited ophthalmic expertise and resources.

References

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Copyright

Copyright © 2025 Mr. R. S. Bhalerao, Rahul Daware, Vaishnavi Dumbare , Nalinee Bodke , Gauri Lipte. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.