Open Access

ARTICLE

Diagnosing Retinal Eye Diseases: A Novel Transfer Learning Approach

Mohammed Salih Ahmed¹, Atta Rahman^2,*, Yahya Alhabboub¹, Khalid Alzahrani¹, Hassan Baragbah¹, Basel Altaha¹, Hussein Alkatout¹, Sardar Asad Ali Biabani^3,4, Rashad Ahmed⁵, Aghiad Bakry²

1 Department of Computer Engineering (CE), College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
2 Department of Computer Science (CS), College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
3 Science Technology Unit, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
4 Deanship of Postgraduate Studies and Research, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
5 Information & Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

* Corresponding Author: Atta Rahman. Email:

Intelligent Automation & Soft Computing 2025, 40, 149-175. https://doi.org/10.32604/iasc.2025.059080

Received 27 September 2024; Accepted 23 December 2024; Issue published 12 February 2025

Abstract

This study rigorously evaluates the potential of transfer learning in diagnosing retinal eye diseases using advanced models such as YOLOv8, Xception, ConvNeXtTiny, and VGG16. All models were trained on the esteemed RFMiD dataset, which includes images classified into six critical categories: Diabetic Retinopathy (DR), Macular Hole (MH), Diabetic Neuropathy (DN), Optic Disc Changes (ODC), Tesselated Fundus (TSLN), and normal cases. The research emphasizes enhancing model performance by prioritizing recall metrics, a crucial strategy aimed at minimizing false negatives in medical diagnostics. To address the challenge of imbalanced data, we implemented effective preprocessing techniques, including cropping, resizing, and data augmentation. The proposed models underwent fine-tuning and were evaluated using established metrics such as accuracy, precision, and recall. The experimental results are compelling, with YOLOv8 achieving the highest recall rates for both normal cases (97.76%) and DR cases (87.10%), demonstrating its reliability in disease screening. In contrast, Xception showed a decline in recall after fine-tuning, particularly in identifying DR and MH cases, highlighting the need for a careful balance between sensitivity and specificity in model training. Notably, both ConvNeXtTiny and VGG16 exhibited significant improvements post-fine-tuning, with VGG16’s recall for normal conditions increasing dramatically from 40.30% to an impressive 89.55%. These findings clearly underscore the potential of utilizing pre-trained models through transfer learning for the effective detection of retinal eye diseases, ultimately contributing to improved patient outcomes in medical diagnostics.

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Keywords

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