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This paper proposes an Adaptive Multi-Strategy Particle Swarm Optimization (AMS-PSO) framework that tunes CNN hyperparameters by assigning each particle to one of three update strategies based on its fitness improvement rate and population diversity. On a large-scale OCT dataset (84,495 images, four pathology classes), AMS-PSO achieves 95.24% test accuracy using only 88 model evaluations, outperforming Bayesian optimization (94.18%), grid search (92.87%), and standard PSO (93.87%). Ablation and benchmark experiments confirm each component\u2019s contribution and the generality of the optimization gains.<\/jats:p>","DOI":"10.1007\/s44163-026-01601-9","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T10:23:14Z","timestamp":1781691794000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated retinal disease classification from OCT images using particle swarm-optimized deep learning"],"prefix":"10.1007","volume":"6","author":[{"given":"Abdelaadim","family":"Khriss","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aissa Kerkour","family":"Elmiad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Badaoui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pankaj","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghanshyam G.","family":"Tejani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"issue":"12","key":"1601_CR1","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1016\/S2214-109X(17)30393-5","volume":"5","author":"RR Bourne","year":"2017","unstructured":"Bourne RR, Flaxman SR, Braithwaite T, Cicinelli MV, Das A, Jonas JB, et al. 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No additional ethics approval was required for this secondary analysis. Informed consent was obtained from all participants by the original dataset authors. No additional consent was required as this study uses only de-identified, publicly available data and involved no direct participant interaction.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"572"}}