{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T12:28:46Z","timestamp":1789129726002,"version":"build-2803163510"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Retinal diseases are a major cause of preventable visual impairment, and optical coherence tomography (OCT) provides high-resolution cross-sectional imaging for retinal assessment. However, reliable interpretation of OCT B-scans remains challenging because of speckle noise, subtle layer-wise changes, and visually similar pathological patterns.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We propose SLight-Net, a lightweight spectral-layer aware network for retinal OCT classification. The model is built on a compact three-stage convolutional backbone and incorporates two OCT-specific modules. The Frequency-Aware Spectral-Spatial Encoder (FASE) integrates local convolution, dilated contextual modeling, and learnable spectral modulation to capture multi-scale structural and frequency-aware retinal features. The Retinal Layer Depth Attention (RLDA) module further introduces a depth-direction anatomical prior to recalibrate feature responses along retinal layers. Deep supervision and exponential moving-average weight updating are used to improve optimization stability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On the OCT-C8 benchmark, SLight-Net achieves 98.21% classification accuracy with only 1.204M parameters. Additional evaluation on OCT2017 shows 99.30% accuracy, suggesting that the model maintains stable performance under a different class setting while remaining compact.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings indicate that OCT-specific spectral and layer-aware priors can support efficient retinal disease classification without relying on large generic backbones, providing a practical basis for lightweight computer-aided OCT analysis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcell.2026.1908247","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T08:11:02Z","timestamp":1785917462000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["SLight-Net: a lightweight spectral-layer aware network for retinal disease detection based on optical coherence tomography"],"prefix":"10.3389","volume":"14","author":[{"given":"Yixiang","family":"Yao","sequence":"first","affiliation":[{"name":"School of Information Engineering, Nanchang University","place":["Nanchang, China"]},{"name":"School of Artificial Intelligence, Nanchang University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Nanchang University","place":["Nanchang, China"]},{"name":"School of Artificial Intelligence, Nanchang University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongli","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong","place":["Pok Fu Lam, Hong Kong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junhua","family":"Hu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Libin","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Wuhan College","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peixin","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongbiao","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhua","family":"Luo","sequence":"additional","affiliation":[{"name":"Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou Institute of Medical Imaging, Ganzhou People\u2019s Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University","place":["Ganzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e130","DOI":"10.1016\/s2214-109x(20)30425-3","article-title":"Trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the global burden of disease study","volume":"9","author":"Bourne","year":"2021","journal-title":"Lancet Global Health"},{"key":"B2","doi-asserted-by":"publisher","first-page":"0","DOI":"10.48130\/vns-0025-0027","article-title":"Artificial intelligence in predicting anti-VEGF treatment response in diabetic macular edema: current progress and future directions","volume":"42","author":"Cao","year":"2025","journal-title":"Vis. 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