{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T09:47:10Z","timestamp":1779356830401,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,2,26]],"date-time":"2019-02-26T00:00:00Z","timestamp":1551139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671272"],"award-info":[{"award-number":["61671272"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Opening Project of Guangdong Province Key Laboratory of Big Data Analysis and Processing","award":["201803"],"award-info":[{"award-number":["201803"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Finetuning pre-trained deep neural networks (DNN) delicately designed for large-scale natural images may not be suitable for medical images due to the intrinsic difference between the datasets. We propose a strategy to modify DNNs, which improves their performance on retinal optical coherence tomography (OCT) images. Deep features of pre-trained DNN are high-level features of natural images. These features harm the training of transfer learning. Our strategy is to remove some deep convolutional layers of the state-of-the-art pre-trained networks: GoogLeNet, ResNet and DenseNet. We try to find the optimized deep neural networks on small-scale and large-scale OCT datasets, respectively, in our experiments. Results show that optimized deep neural networks not only reduce computational burden, but also improve classification accuracy.<\/jats:p>","DOI":"10.3390\/a12030051","type":"journal-article","created":{"date-parts":[[2019,2,26]],"date-time":"2019-02-26T11:00:44Z","timestamp":1551178844000},"page":"51","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images"],"prefix":"10.3390","volume":"12","author":[{"given":"Qingge","family":"Ji","sequence":"first","affiliation":[{"name":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China"},{"name":"Guangdong Key Laboratory of Big Data Analysis and Processing, Guangdong 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China"},{"name":"Guangdong Key Laboratory of Big Data Analysis and Processing, Guangdong 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjie","family":"He","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China"},{"name":"Guangdong Key Laboratory of Big Data Analysis and Processing, Guangdong 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7155-8261","authenticated-orcid":false,"given":"Yankui","family":"Sun","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Big Data Analysis and Processing, Guangdong 510006, China"},{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1136\/bjophthalmol-2013-304033","article-title":"Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990\u20132010","volume":"98","author":"Bourne","year":"2014","journal-title":"Br. 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