{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:09:51Z","timestamp":1759968591644,"version":"build-2065373602"},"reference-count":49,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alzheimer's &amp;amp; Dementia"],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The diagnosis of mild cognitive impairment (MCI), which is deemed a formative phase of dementia, may greatly assist clinicians in delaying its headway toward dementia. This article proposes a deep learning approach based on a triploid genetic algorithm, a proposed variant of genetic algorithms, for classifying MCI converts and non\u2010converts using structural magnetic resonance imaging data. It also explores the effect of the choice of activation functions and that of the selection of hyper\u2010parameters on the performance of the model. The proposed work is a step toward automated convolutional neural networks. The performance of the proposed method is measured in terms of accuracy and empirical studies exhibit the preeminence of our proposed method over the existing ones. The proposed model results in a maximum accuracy of 0.97961. Thus, it may contribute to the effective diagnosis of MCI and may prove important in clinical settings.<\/jats:p>","DOI":"10.1002\/alz.12565","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T05:04:39Z","timestamp":1643691879000},"page":"2283-2291","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Triploid genetic algorithm for convolutional neural network\u2013based diagnosis of mild cognitive impairment"],"prefix":"10.1002","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8151-0501","authenticated-orcid":false,"given":"Harsh","family":"Bhasin","sequence":"first","affiliation":[{"name":"School of Computer and Systems Sciences Jawaharlal Nehru University  Delhi India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R. K.","family":"Agrawal","sequence":"additional","affiliation":[{"name":"School of Computer and Systems Sciences Jawaharlal Nehru University  Delhi India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"for Alzheimer's Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,2]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"https:\/\/alzcnfl.wordpress.com\/2016\/04\/28\/alzheimers\u2010a\u2010real\u2010love\u2010story\/Accessed April 25 2021."},{"key":"e_1_2_7_3_1","unstructured":"The Dementias: Hope Through Research | National Institute of Neurological Disorders and Stroke.https:\/\/www.ninds.nih.gov\/Disorders\/Patient\u2010Caregiver\u2010Education\/Hope\u2010Through\u2010Research\/Dementia\u2010Hope\u2010Through\u2010Research. Accessed January 1 2019."},{"key":"e_1_2_7_4_1","unstructured":"World Alzheimer Report 2018 The state of the art of dementia research: New (2018). Alzheimer's disease international Chicago.https:\/\/www.alzint.org\/u\/WorldAlzheimerReport2018.pdf"},{"key":"e_1_2_7_5_1","unstructured":"Mild Cognitive Impairment https:\/\/alz.org\/alzheimers\u2010dementia\/what\u2010is\u2010dementia\/related_conditions\/mild\u2010cognitive\u2010impairment Accessed January 1 2019."},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11920-012-0291-x"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1159\/000354370"},{"key":"e_1_2_7_8_1","doi-asserted-by":"crossref","unstructured":"SukH\u2010I ShenD Deep learning\u2010based feature representation for AD\/MCI classification.The International Conference on Medical Image Computing and Computer\u2010Assisted Intervention 2013;583\u2010590.","DOI":"10.1007\/978-3-642-40763-5_72"},{"key":"e_1_2_7_9_1","doi-asserted-by":"crossref","unstructured":"SinghS et\u00a0al.Deep Learning based Classification of FDG\u2010PET Data for Alzheimers Disease Categories.Proc. SPIE\u2013 Int. Soc. Opt. Eng.;2017:10572.","DOI":"10.1117\/12.2294537"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.05.036"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/hipo.20626"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.11.066"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-012-0462-0"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.22156"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2014.04.006"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-014-2123-y"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2016.2635144"},{"key":"e_1_2_7_18_1","first-page":"1","article-title":"A combination of 3\u2010D discrete wavelet transform and 3\u2010D local binary pattern for classification of mild cognitive impairment","volume":"20","author":"Bhasin H","year":"2020","journal-title":"BMC Med Inform DecisMak"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jns.2011.12.011"},{"key":"e_1_2_7_20_1","unstructured":"Texture Analysis Using the Gray\u2010Level Co\u2010Occurrence Matrix (GLCM) \u2010 MATLAB & Simulink \u2010 MathWorks United Kingdom."},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1017623"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/0031-3203(95)00067-4"},{"key":"e_1_2_7_23_1","doi-asserted-by":"crossref","unstructured":"OjalaT Pietik\u00e4inenM M\u00e4enp\u00e4\u00e4T A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification The International Conference on Advances in Pattern Recognition 2001;399\u2010408.","DOI":"10.1007\/3-540-44732-6_41"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.192463"},{"key":"e_1_2_7_25_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_7_26_1","unstructured":"Convolutional Neural Networks for Visual Recognition Available:https:\/\/cs231n.github.io\/convolutional\u2010networks\/[Accessed: 25\u2010Feb\u20102021]."},{"key":"e_1_2_7_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2015.2429556"},{"key":"e_1_2_7_28_1","unstructured":"SitzmannetV.Implicit Neural Representations with Periodic Activation Functions. ArXiv abs\/2006.09661 2020.https:\/\/arxiv.org\/pdf\/2006.09661.pdf"},{"key":"e_1_2_7_29_1","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0b013e3181cb3e25"},{"key":"e_1_2_7_30_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2015.00307"},{"key":"e_1_2_7_31_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.460020402"},{"key":"e_1_2_7_32_1","unstructured":"NgKP WongKC A New Diploid Scheme and Dominance Change Mechanism for Non\u2010Stationary Function Optimization Inthe Proceedings of the 6th International Conference on Genetic Algorithms EshelmanL J(Ed.) Morgan Kaufmann Publishers Inc. San Francisco CA USA 1994 pp.159\u2010166."},{"key":"e_1_2_7_33_1","unstructured":"RyanC Degree of Oneness inthe proceedings of the 1994ECAI workshop of Genetic Algorithm 1994."},{"key":"e_1_2_7_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2018.101645"},{"key":"e_1_2_7_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.01.008"},{"key":"e_1_2_7_36_1","doi-asserted-by":"crossref","unstructured":"SinghS SrivastavaA MiL et\u00a0al. Deep\u2010learning\u2010based classification of FDG\u2010PET data for Alzheimer's disease categories inProceedings of the 13th International Conference on Medical Information Processing and Analysis (Bellingham WA:International Society for Optics and Photonics) 2017.https:\/\/doi.org\/10.1117\/12.2294537.","DOI":"10.1117\/12.2294537"},{"key":"e_1_2_7_37_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-22871-z"},{"key":"e_1_2_7_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.02.002"},{"key":"e_1_2_7_39_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065716500258"},{"volume-title":"adaptation in Natural and Artificial Systems","year":"1975","author":"Holland JH","key":"e_1_2_7_40_1"},{"volume-title":"Evolutionary Computing, A Unified Approach","year":"2006","author":"Kenneth A","key":"e_1_2_7_41_1"},{"volume-title":"Genetic Algorithms in Search, Optimization and Machine Learning","year":"1989","author":"Goldberg DE","key":"e_1_2_7_42_1"},{"key":"e_1_2_7_43_1","unstructured":"HartlD (2011).Essential Genetics: A Genomics Perspective. Jones & Bartlett Learning. p.177. ISBN 978\u20100\u20107637\u20107364\u20109"},{"volume-title":"Artificial Genetic Adaptation in Computer Control Systems","year":"1971","author":"Hollstein RB","key":"e_1_2_7_44_1"},{"key":"e_1_2_7_45_1","first-page":"187","article-title":"Real\u2010coded genetic algorithms and interval\u2010schemata","volume":"2","author":"Eshelman LJ","year":"1993","journal-title":"Found Genetic Alg"},{"key":"e_1_2_7_46_1","first-page":"815","article-title":"Experimental study on population based incremental learning algorithm for dynamic optimization problem","author":"Yang S","year":"2008","journal-title":"Soft Computing"},{"key":"e_1_2_7_47_1","unstructured":"YangS.On the design of diploid genetic algorithms for problem optimization in dynamic environments. InProceedings of IEEE International Conference on Evolutionary Computation 2006;1362\u20101369."},{"key":"e_1_2_7_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-004-0422-3"},{"key":"e_1_2_7_49_1","article-title":"How do seedless fruits arise and how are they propagated?","author":"Burr B","year":"2020","journal-title":"Sci Am"},{"key":"e_1_2_7_50_1","doi-asserted-by":"crossref","unstructured":"SarrafS&TofighiGfor the Alzheimer's Disease Neuroimaging Initiative DeepAD: Alzheimer's Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI 2016;1\u201015.","DOI":"10.1101\/070441"}],"container-title":["Alzheimer's &amp; Dementia"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/alz.12565","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/alz.12565","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/alz-journals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/alz.12565","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T23:40:09Z","timestamp":1759880409000},"score":1,"resource":{"primary":{"URL":"https:\/\/alz-journals.onlinelibrary.wiley.com\/doi\/10.1002\/alz.12565"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2]]},"references-count":49,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["10.1002\/alz.12565"],"URL":"https:\/\/doi.org\/10.1002\/alz.12565","archive":["Portico"],"relation":{},"ISSN":["1552-5260","1552-5279"],"issn-type":[{"type":"print","value":"1552-5260"},{"type":"electronic","value":"1552-5279"}],"subject":[],"published":{"date-parts":[[2022,2]]},"assertion":[{"value":"2021-07-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-03","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-02-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}