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Open Access

ARTICLE

ECSA-Net: A Lightweight Attention-Based Deep Learning Model for Eye Disease Detection

Sara Tehsin1,*, Muhammad John Abbas2, Inzamam Mashood Nasir1, Fadwa Alrowais3, Reham Abualhamayel4, Abdulsamad Ebrahim Yahya5, Radwa Marzouk6
1 Faculty of Informatics, Kaunas University of Technology, Kaunas, Lithuania
2 Department of Computer Science, HITEC University Taxila, Taxila, Pakistan
3 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
4 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
5 Department of Information Technology, College of Computing and Information Technology, Northern Border University, Arar, Saudi Arabia
6 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
* Corresponding Author: Sara Tehsin. Email: email
(This article belongs to the Special Issue: Bridging the Gap: AutoML and Explainable AI for Industrial and Healthcare Innovations)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.076515

Received 21 November 2025; Accepted 07 January 2026; Published online 04 February 2026

Abstract

Globally, diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness. To treat these vision disorders effectively, proper diagnosis must occur in a timely manner, and with conventional methods such as fundus photography, optical coherence tomography (OCT), and slit-lamp imaging, much depends on an expert’s interpretation of the images, making the systems very labor-intensive to operate. Moreover, clinical settings face difficulties with inter-observer variability and limited scalability with these diagnostic devices. To solve these problems, we have developed the Efficient Channel-Spatial Attention Network (ECSA-Net), a new deep learning-based methodology that integrates lightweight channel- and spatial-attention modules into a convolutional neural network. Ultimately, ECSA-Net improves the efficiency of computational resource use while enhancing discriminative feature extraction from retinal images. The ECSA-Net methodology was validated by conducting a series of classification accuracy tests using two publicly available eye disease datasets and was benchmark against a number of different pretrained convolutional neural network (CNN) architectures. The results showed that the ECSA-Net achieved classification accuracies of 60.00% and 69.92%, respectively, while using only a compact architecture with 0.56 million parameters. This represents a reduction in parameter size by a factor of 14× to 247× compared to other pretrained models. Additionally, the attention modules added to the architecture significantly increased sensitivity to disease-relevant regions of the retina while maintaining low computational cost, making ECSA-Net a viable option for real-time clinical use. ECSA-Net is both efficient and accurate in automating the classification of eye diseases, combining high performance with the ethical considerations of medical artificial intelligence (AI) deployment. The ECSA-Net framework mitigates algorithmic bias in training datasets and protects individuals’ privacy and transparency in decision-making, thereby facilitating human-AI collaboration. The two areas of technical performance and ethical integration are needed for the responsible and scalable use of ECSA-Net in a variety of ophthalmic care settings.

Keywords

Channel-spatial attention; explainable AI; eye disease classification; fairness in diagnostics; lightweight deep learning; transparency in healthcare

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