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While deep learning has shown strong potential in diagnosing such conditions using optical coherence tomography (OCT) images, many existing models are highly complex and computationally intensive, making them impractical for deployment on edge devices in clinical settings. To address this issue, we propose a diagnostic framework based on a lightweight deep\u2010learning architecture specifically designed for efficient retinal disease detection. Our model integrates two key components: a lite convolution block, utilizing depthwise separable convolutions for computational efficiency, and a global\u2010local fusion block, which captures both fine\u2010grained local and contextual global features. A squeeze\u2010and\u2010excitation mechanism further refines channel\u2010wise feature importance, all while keeping the parameter count to only 0.27 million. We evaluate our model on three benchmark datasets\u2010OCT 2017, OCT C8 and OCTDL\u2010 achieving accuracies of 99.70%, 95.00% and 97.26%, respectively. Our approach demonstrates strong and stable performance validated by confusion matrix and ROC analysis, while Grad\u2010CAM visualizations enhance interpretability, collectively aiming to offer a practical and efficient solution for real\u2010time retinal disease\u00a0diagnosis.<\/jats:p>","DOI":"10.1049\/htl2.70059","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T15:05:34Z","timestamp":1770908734000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Toward Efficient Identification of Retinal Diseases: A Lightweight Convolutional Neural Network\u2010Based Approach Using Optical Coherence Tomography"],"prefix":"10.1049","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2106-8648","authenticated-orcid":false,"given":"Utsab","family":"Saha","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2882-8427","authenticated-orcid":false,"given":"Puja","family":"Saha","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2922-8696","authenticated-orcid":false,"given":"MD Jahin","family":"Alam","sequence":"additional","affiliation":[{"name":"School of Data and Sciences BRAC University Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9302-6215","authenticated-orcid":false,"given":"Maruf","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"issue":"3","key":"e_1_2_11_2_1","first-page":"264","article-title":"Promising Artificial Intelligence\u2010Machine Learning\u2010Deep Learning Algorithms in Ophthalmology","volume":"8","author":"Balyen L.","year":"2019","journal-title":"The Asia\u2010Pacific Journal of Ophthalmology"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.412156"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107146"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108116"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.127721"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113625"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroscience.2025.04.010"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-022-00793-7"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.8.000579"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-019-02066-y"},{"key":"e_1_2_11_13_1","doi-asserted-by":"crossref","unstructured":"M.Awais H.M\u00fcller T. 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