Prospective Detection of Diabetic Retinopathy Using Modified CNN Models on Fundus Images: A Study at Al-Noor Institute, Al-Nasiriya

Atyaf Jarullah Yaseen
International Journal of Computational and Electronic Aspects in Engineering
Volume 6: Issue 3, July 2025, pp 168-179

Author's Information
Atyaf Jarullah Yaseen1 
Corresponding Author
1Department of Computer Science , College of Computer Science and Mathematics,University of Thi-Qar,Iraq
atyafjarallah82@utq.edu.iq
Research Paper -- Peer Review
First online on – 25 July 2025

Open Access article under Creative Commons License

Cite this article –Atyaf Jarullah Yaseenl, “Prospective Detection of Diabetic Retinopathy Using Modified CNN Models on Fundus Images: A Study at Al-Noor Institute, Al-Nasiriya”, International Journal of Computational and Electronic Aspects in Engineering, RAME Publishers, Volume 6, Issue 3, pp. 168-179, 2025.
https://doi.org/10.26706/ijceae.6.3.20250607

Abstract:-
Diabetic retinopathy (DR) is considered to be the most common microvascular complication with diabetes mellitus and continues to be the main disease that causes vision impairment and blindness all over the world. Early-stage outcomes are, however, difficult to identify; they require highly qualified clinical retinal fundus photos interpretation to detect DR at an opportune stage in order to avert visual disability that would most probably be irreversible. The aim of this prospective study was to consider a deep learning-based diagnostic model, which is based on the modified convolutional neural network (CNN) trained and tested on a proprietary dataset and estimated in Al-Noor Institute in Al-Nasiriya in the Ophthalmology department. The goal of the model was to evaluate the quality of input fundus images, and group them in such categories as DR-positive, and DR-negative. Clinical ophthalmologists were used to check the production of the model and certify the results of model accuracy. The study used 398 patients (232 males and 166 femals) screened over a five weeks period. Compared to the expert-labeled ground truth, the proposed model had an accuracy of 93.72%, sensitivity of 97.30%, and a specificity of 92.90% initialization. This evidence underlines the feasibility of deep learning applications in helping to detect diabetic retinopathy early and especially in low-resource environments.
Index Terms:-
Diabetic retinopathy. Fundus imaging, convolutional neural networks, deep learning, automated diagnosis, ophthalmology
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