{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T17:34:47Z","timestamp":1777484087122,"version":"3.51.4"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,5,31]]},"abstract":"<jats:p>\n            Alzheimer\u2019s Disease (AD) is an irreversible neurogenerative disorder that undergoes progressive decline in memory and cognitive function and is characterized by structural brain Magnetic Resonance Images (sMRI). In recent years, sMRI data has played a vital role in the evaluation of brain anatomical changes, leading to early detection of AD through deep networks. The existing AD problems such as preprocessing complexity and unreliability are major concerns at present. To overcome these, a model (\n            <jats:italic>\n              FE\n              <jats:sub>ES<\/jats:sub>\n              C\n              <jats:sub>TL<\/jats:sub>\n            <\/jats:italic>\n            ) has been proposed with an entropy slicing for feature extraction and Transfer Learning for classification. In the present study, the entropy image slicing method is attempted for selecting the most informative MRI slices during training stages. The ADNI dataset is trained on Transfer Learning adopted by VGG-16 network for classifying the AD with normal individuals. The experimental results reveal that the proposed model has achieved an accuracy level of 93.05%, 86.39%, 92.00% for binary classifications (AD\/MCI, MCI\/CN, AD\/CN) and 93.12% for ternary classification (AD\/MCI\/CN), respectively, and henceforth the efficiency in diagnosing AD is proved through comparative analysis.\n          <\/jats:p>","DOI":"10.1145\/3383749","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T12:38:12Z","timestamp":1594125492000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["Entropy Slicing Extraction and Transfer Learning Classification for Early Diagnosis of Alzheimer Diseases with sMRI"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6718-0462","authenticated-orcid":false,"given":"S. Sambath","family":"Kumar","sequence":"first","affiliation":[{"name":"Research Scholar, Department of Computer Science, Pondicherry University, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Nandhini","sequence":"additional","affiliation":[{"name":"Associate Professor, Department of Computer Science, Pondicherry University, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1214\/12-AOS1000"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2016.06.003"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.2000.0582"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2019.01.010"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2016.09.019"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11682-018-9846-8"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2012.10.002"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.06.013"},{"key":"e_1_2_1_9_1","volume-title":"A survey of neural network-based cancer prediction models from microarray data. Artif. Intell. Med. 97","author":"Daoud Maisa","year":"2019"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the SAI Computing Conference (SAI\u201916)","author":"Xiaohong"},{"key":"e_1_2_1_11_1","volume-title":"et\u00a0al","author":"Gauthier Serge","year":"2006"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2017.09.110"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP\u201917)","author":"Gunawardena K. A. N. N. P."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jns.2009.10.022"},{"key":"e_1_2_1_15_1","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"Hastie Trevor"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2019.00509"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.21049"},{"key":"e_1_2_1_20_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2014"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0933-3657(01)00077-X"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2018.12.003"},{"key":"e_1_2_1_23_1","first-page":"21","article-title":"Analysis of surface-based morphometric of hippocampal subfield volumetry in Alzheimer\u2019s disease and MCI","volume":"10","author":"Sambath Kumar S.","year":"2019","journal-title":"Institute of Integrative Omics and Applied Biotechnology"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2016.00077"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2018.09.009"},{"key":"e_1_2_1_27_1","volume-title":"Deep ordinal ranking for multi-category diagnosis of Alzheimer\u2019s disease using hippocampal MRI data. arXiv preprint arXiv:1709.01599","author":"Li Hongming","year":"2017"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-69179-4_36"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-018-9362-4"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2014.2372011"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2018.11.008"},{"key":"e_1_2_1_32_1","volume-title":"Proceedings of the International Conference on Machine Learning","volume":"30","author":"Maas Andrew L."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.070039597"},{"key":"e_1_2_1_34_1","volume-title":"Proceedings of the 27th International Conference on Machine Learning. 807--814","author":"Nair Vinod"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2017.11.008"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2017.03.006"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1001\/archneur.56.3.338"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2014.11.001"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.06.077"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-02267-3_17"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cogsys.2018.12.007"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A1809"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.11.025"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11682-015-9437-x"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.09.069"},{"key":"e_1_2_1_48_1","volume-title":"Alzheimer\u2019s Disease Neuroimaging Initiative, et\u00a0al","author":"Zhang Daoqiang","year":"2012"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.01.008"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.3390\/app8081372"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3383749","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3383749","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:33:19Z","timestamp":1750199599000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3383749"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,21]]},"references-count":50,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,5,31]]}},"alternative-id":["10.1145\/3383749"],"URL":"https:\/\/doi.org\/10.1145\/3383749","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,21]]},"assertion":[{"value":"2019-10-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}