| Title |
ECSA-Net: a lightweight attention-based deep learning model for eye disease detection |
| Authors |
Tehsin, Sara ; Abbas, Muhammad John ; Nasir, Inzamam Mashood ; Alrowais, Fadwa ; Abualhamayel, Reham ; Yahya, Abdulsamad Ebrahim ; Marzouk, Radwa |
| DOI |
10.32604/cmc.2026.076515 |
| Full Text |
|
| Is Part of |
Computers, materials and continua.. Henderson, NV : Tech Science Press. 2026, vol. 87, iss. 2, art. no. 56, p. 1-34.. ISSN 1546-2218. eISSN 1546-2226 |
| Keywords [eng] |
Channel-spatial attention ; explainable AI ; eye disease classification ; fairness in diagnostics ; lightweight deep learning ; transparency in healthcare |
| Abstract [eng] |
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. |
| Published |
Henderson, NV : Tech Science Press |
| Type |
Journal article |
| Language |
English |
| Publication date |
2026 |
| CC license |
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