{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T15:21:00Z","timestamp":1784647260684,"version":"3.55.0"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"2s","license":[{"start":{"date-parts":[[2020,4,30]],"date-time":"2020-04-30T00:00:00Z","timestamp":1588204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"crossref","award":["Y18F010018"],"award-info":[{"award-number":["Y18F010018"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Guangxi Key Laboratory of Trusted Software","award":["kx201901"],"award-info":[{"award-number":["kx201901"]}]},{"name":"Henan Key Research and Development Project","award":["182102310629"],"award-info":[{"award-number":["182102310629"]}]},{"name":"National Key Research and Development Plan","award":["2017YFB1103202"],"award-info":[{"award-number":["2017YFB1103202"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61602250, 11502090, U1711263, U1811264"],"award-info":[{"award-number":["61602250, 11502090, U1711263, U1811264"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2020,4,30]]},"abstract":"<jats:p>\n            (\n            <jats:bold>Aim<\/jats:bold>\n            ) Multiple sclerosis is a neurological condition that may cause neurologic disability. Convolutional neural network can achieve good results, but tuning hyperparameters of CNN needs expert knowledge and are difficult and time-consuming. To identify multiple sclerosis more accurately, this article proposed a new transfer-learning-based approach. (\n            <jats:bold>Method<\/jats:bold>\n            ) DenseNet-121, DenseNet-169, and DenseNet-201 neural networks were compared. In addition, we proposed the use of a composite learning factor (CLF) that assigns different learning factor to three types of layers: early frozen layers, middle layers, and late replaced layers. How to allocate layers into those three layers remains a problem. Hence, four transfer learning settings (viz., Settings A, B, C, and D) were tested and compared. A precomputation method was utilized to reduce the storage burden and accelerate the program. (\n            <jats:bold>Results<\/jats:bold>\n            ) We observed that DenseNet-201-D (the layers from CP to T3 are frozen, the layers of D4 are updated with learning factor of 1, and the final new layers of FCL are randomly initialized with learning factor of 10) can achieve the best performance. The sensitivity, specificity, and accuracy of DenseNet-201-D was 98.27\u00b1 0.58, 98.35\u00b1 0.69, and 98.31\u00b1 0.53, respectively. (\n            <jats:bold>Conclusion<\/jats:bold>\n            ) Our method gives better performances than state-of-the-art approaches. Furthermore, this composite learning rate gives superior results to traditional simple learning factor (SLF) strategy.\n          <\/jats:p>","DOI":"10.1145\/3341095","type":"journal-article","created":{"date-parts":[[2020,6,22]],"date-time":"2020-06-22T02:49:20Z","timestamp":1592794160000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":189,"title":["DenseNet-201-Based Deep Neural Network with Composite Learning Factor and Precomputation for Multiple Sclerosis Classification"],"prefix":"10.1145","volume":"16","author":[{"given":"Shui-Hua","family":"Wang","sequence":"first","affiliation":[{"name":"School of Architecture Building and Civil Engineering, Loughborough University, Loughborough, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Dong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Leicester, Leicester, Leicestershire, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,6,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000000768"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s15010-018-1196-3"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10072-018-3660-3"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.2174\/0929867323666160406121218"},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"E. S. Gromisch R. D. Kerns R. Czlapinski B. Beenken J. Otis A. C. Lo and J. Beauvais. 2019. Cognitive behavioral therapy for the management of multiple sclerosis--related pain: A randomized clinical trial. International Journal of MS Care.  E. S. Gromisch R. D. Kerns R. Czlapinski B. Beenken J. Otis A. C. Lo and J. Beauvais. 2019. Cognitive behavioral therapy for the management of multiple sclerosis--related pain: A randomized clinical trial. International Journal of MS Care.","DOI":"10.7224\/1537-2073.2018-023"},{"key":"e_1_2_1_6_1","first-page":"16","article-title":"Caring for a patient with multiple sclerosis","volume":"23","author":"Porten L.","year":"2017","journal-title":"Kai Tiaki: Nursing New Zealand"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2040446"},{"key":"e_1_2_1_8_1","first-page":"4","article-title":"Multiple sclerosis identification based on fractional fourier entropy and a modified jaya algorithm","volume":"20","author":"Cheng H.","year":"2018","journal-title":"Entropy"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the 23rd International Conference on Digital Signal Processing (DSP). 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