ORIGINAL RESEARCH article
Front. Med.
Sec. Ophthalmology
Enhancing Fundus Image Analysis for Diabetic Retinopathy Using CheXNet with CBAM and Grad-CAM Visualization
Wedad Al-Dolat 1
Salem Alhatamleh 1
Noor Alqudah 2
Amro Alhazimi 3
Mohammad Amin 1
Aseel Daamseh 2
Rola Madain 2
Raghad Malkawi 2
Rami Al- Omari 1
Faisal Almarek 3
Sarah husam aljefri 3
1. Yarmouk University, Irbid, Jordan
2. Jordan University of Science and Technology, Irbid, Jordan
3. Imam Muhammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
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Abstract
Diabetic retinopathy (DR) is a leading cause of vision impairment among people with diabetes; therefore, early detection and the right diagnosis are necessary to save sight. Unfortunately, making the models for the complex fundus images' understanding and diagnosis is still a major challenge. In the paper, a new method is proposed for the diabetic retinopathy diagnosis from fundus images employing the hybrid model named CheXNet_CBAM. The model is an upgrade of the DenseNet121 architecture. For better feature representation, a convolutional block attention module (CBAM) is added to the CheXNet model. This module integrates both channel and spatial attention, allowing the network to put more attention on the retinal locations that are clinically significant. The Grad-CAM technique is also employed to produce the heat maps, which show the areas of the fundus images that are most important for classification. The proposed model is evaluated against deep learning models (CheXNet, DenseNet121, MobileNetV2, VGG19, and ResNet50). The suggested CheXNet_CBAM model surpasses the other models in diabetic retinopathy diagnosis from fundus images with a remarkably high accuracy of 96.12% over the APTOS-2019 dataset and 96.33% for the DDR dataset. CheXNet_CBAM not only offers a more reliable diagnostic foundation for doctors, but also its excellent diabetic retinopathy classification skills can significantly help patients with early detection and treatment of the ailment. This presents a remarkable clinical impact in the diabetic retinopathy diagnosis area.
Summary
Keywords
deep learning, Diabetic Retinopathy, Fundus imaging, Grad-CAM, image classification
Received
25 October 2025
Accepted
06 February 2026
Copyright
© 2026 Al-Dolat, Alhatamleh, Alqudah, Alhazimi, Amin, Daamseh, Madain, Malkawi, Al- Omari, Almarek and aljefri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Amro Alhazimi
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