A Lesion-Based Diabetic Retinopathy Detection Through Hybrid Deep Learning Model

Jabbar, Ayesha and Liaqat, Hannan Bin and Akram, Aftab and Sana, Muhammad Usman and Dominguez Azpíroz, Irma and de la Torre Díez, Isabel and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, irma.dominguez@unini.edu.mx, UNSPECIFIED, UNSPECIFIED (2024) A Lesion-Based Diabetic Retinopathy Detection Through Hybrid Deep Learning Model. IEEE Access, 12. pp. 40019-40036. ISSN 2169-3536

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Abstract

Diabetic retinopathy (DR) can be defined as visual impairment caused by prolonged diabetes affecting the blood vessels in the retina. Globally, it stands as the primary contributor to blindness, impacting approximately 191 million individuals. While prior research has addressed DR classification using retinal fundus images, existing methods often focus on isolated lesion detection, lacking a comprehensive framework for the simultaneous identification of all lesions. Previous studies concentrated on early-stage features like exudates, aneurysms, hemorrhages, and blood vessels, sidelining severe-stage lesions such as cotton wool spots, venous beading, very severe intraretinal microvascular abnormalities (IRMA), diffuse intraretinal hemorrhages, capillary degeneration, highly activated microglia, and retinal pigment epithelium (RPE) damage. In this study, a deep learning approach is proposed to classify DR fundus images by severity levels, utilizing GoogleNet and ResNet models based on adaptive particle swarm optimizer (APSO), for enhanced feature extraction. The extracted features from the hybrid model are further used with different machine learning models like random forest, support vector machine, decision tree, and linear regression models. Experimental results showcased the proposed hybrid framework outperforming advanced approaches with a remarkable 94% accuracy on the benchmark dataset. This method demonstrates potential enhancements in precision, recall, accuracy, and F1 score for different DR severity levels.

Item Type:	Article
Uncontrolled Keywords:	Diabetic retinopathy, fundus images, transfer learning, hybrid learning, machine learning, lesion detection.
Subjects:	Subjects > Engineering
Divisions:	Europe University of Atlantic > Research > Articles and books Ibero-american International University > Research > Articles and books Ibero-american International University > Research > Articles and Books
Depositing User:	Sr Bibliotecario
Date Deposited:	07 Jul 2026 09:23
Last Modified:	07 Jul 2026 09:23
URI:	https://repositorio.funiber.org/id/eprint/28597

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