{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:54:18Z","timestamp":1772042058999,"version":"3.50.1"},"reference-count":16,"publisher":"Springer Science and Business Media LLC","issue":"S5","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T00:00:00Z","timestamp":1636329600000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003711","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["MOST 109-2221-E-037-005"],"award-info":[{"award-number":["MOST 109-2221-E-037-005"]}],"id":[{"id":"10.13039\/501100003711","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003711","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["MOST 109-2221-E-153-005-MY3"],"award-info":[{"award-number":["MOST 109-2221-E-153-005-MY3"]}],"id":[{"id":"10.13039\/501100003711","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>The prevalence of chronic disease is growing in aging societies, and artificial-intelligence\u2013assisted interpretation of macular degeneration images is a topic that merits research. This study proposes a residual neural network (ResNet) model constructed using uniform design. The ResNet model is an artificial intelligence model that classifies macular degeneration images and can assist medical professionals in related tests and classification tasks, enhance confidence in making diagnoses, and reassure patients. However, the various hyperparameters in a ResNet lead to the problem of hyperparameter optimization in the model. This study employed uniform design\u2014a systematic, scientific experimental design\u2014to optimize the hyperparameters of the ResNet and establish a ResNet with optimal robustness.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>An open dataset of macular degeneration images (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/data.mendeley.com\/datasets\/rscbjbr9sj\/3\">https:\/\/data.mendeley.com\/datasets\/rscbjbr9sj\/3<\/jats:ext-link>) was divided into training, validation, and test datasets. According to accuracy, false negative rate, and signal-to-noise ratio, this study used uniform design to determine the optimal combination of ResNet hyperparameters. The ResNet model was tested and the results compared with results obtained in a previous study using the same dataset. The ResNet model achieved higher optimal accuracy (0.9907), higher mean accuracy (0.9848), and a lower mean false negative rate (0.015) than did the model previously reported. The optimal ResNet hyperparameter combination identified using the uniform design method exhibited excellent performance.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The high stability of the ResNet model established using uniform design is attributable to the study\u2019s strict focus on achieving both high accuracy and low standard deviation. This study optimized the hyperparameters of the ResNet model by using uniform design because the design features uniform distribution of experimental points and facilitates effective determination of the representative parameter combination, reducing the time required for parameter design and fulfilling the requirements of a systematic parameter design process.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-021-04085-9","type":"journal-article","created":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T15:05:43Z","timestamp":1636383943000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks"],"prefix":"10.1186","volume":"22","author":[{"given":"Wen-Hsien","family":"Ho","sequence":"first","affiliation":[]},{"given":"Tian-Hsiang","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Po-Yuan","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Jyh-Horng","family":"Chou","sequence":"additional","affiliation":[]},{"given":"Hong-Siang","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Li-Chung","family":"Chi","sequence":"additional","affiliation":[]},{"given":"Fu-I","family":"Chou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1531-5027","authenticated-orcid":false,"given":"Jinn-Tsong","family":"Tsai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,8]]},"reference":[{"key":"4085_CR1","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1159\/000337156","volume":"227","author":"GE Lang","year":"2012","unstructured":"Lang GE. 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