{"id":"https://openalex.org/W7125003907","doi":"https://doi.org/10.1109/bsn66969.2025.11337461","title":"Comparative Analysis of Diffusion Models for Enhancing Alzheimer's Disease Classification","display_name":"Comparative Analysis of Diffusion Models for Enhancing Alzheimer's Disease Classification","publication_year":2025,"publication_date":"2025-11-03","ids":{"openalex":"https://openalex.org/W7125003907","doi":"https://doi.org/10.1109/bsn66969.2025.11337461"},"language":null,"primary_location":{"id":"doi:10.1109/bsn66969.2025.11337461","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bsn66969.2025.11337461","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 21st International Conference on Body Sensor Networks (BSN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020141115","display_name":"Jose Gabriel Gonzalez Nunez","orcid":null},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jose Gabriel Gonzalez Nunez","raw_affiliation_strings":["University of Central Florida,Department of Electrical and Computer Engineering,FL,USA,32816"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida,Department of Electrical and Computer Engineering,FL,USA,32816","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026644166","display_name":"Laura J. Brattain","orcid":null},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Laura J. Brattain","raw_affiliation_strings":["University of Central Florida,Department of Electrical and Computer Engineering,FL,USA,32816"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida,Department of Electrical and Computer Engineering,FL,USA,32816","institution_ids":["https://openalex.org/I106165777"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I106165777"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.64582586,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10009","display_name":"Dementia and Cognitive Impairment Research","score":0.820900022983551,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10009","display_name":"Dementia and Cognitive Impairment Research","score":0.820900022983551,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.01759999990463257,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.016200000420212746,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49810001254081726},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4830999970436096},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4375},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.3917999863624573},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.3774999976158142},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.37439998984336853},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.33480000495910645},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.3346000015735626}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6898000240325928},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6148999929428101},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5511999726295471},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49810001254081726},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4830999970436096},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4375},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.3917999863624573},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3774999976158142},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3278999924659729},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2973000109195709},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bsn66969.2025.11337461","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bsn66969.2025.11337461","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 21st International Conference on Body Sensor Networks (BSN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W4297828509","https://openalex.org/W4312443924","https://openalex.org/W4389427155","https://openalex.org/W4403069260","https://openalex.org/W4409180122","https://openalex.org/W4409367393"],"related_works":[],"abstract_inverted_index":{"Early":[0],"and":[1,13,35,42,79,82,104,144,180,202],"accurate":[2],"detection":[3],"of":[4,38,44,68,127,146,171,174,178,182,193,196,200,204],"Alzheimer's":[5],"disease":[6],"(AD)":[7],"is":[8,26],"vital":[9],"for":[10,23,86,150,165],"timely":[11],"intervention":[12],"better":[14],"patient":[15],"outcomes.":[16],"However,":[17],"training":[18,46,151],"machine":[19],"learning":[20],"(ML)":[21],"models":[22,56,78,85,120,161],"this":[24,58,72],"purpose":[25],"challenging":[27],"due":[28],"to":[29,123,140],"the":[30,36,45,105,117,142,159,166,185,188,212,217],"limited":[31],"medical":[32],"images":[33,63,149,164],"available":[34],"imbalance":[37],"classes.":[39],"The":[40,89,154],"size":[41],"quality":[43],"dataset":[47],"directly":[48],"affect":[49],"model":[50,92],"performance.":[51],"Recent":[52],"advances":[53],"in":[54],"diffusion":[55,77,91],"mitigate":[57],"limitation":[59],"by":[60,158],"generating":[61],"synthetic":[62,148,163],"from":[64,211],"a":[65,94,101,108,137],"small":[66],"sample":[67],"real":[69],"images.":[70,219],"In":[71],"work,":[73],"we":[74],"adapted":[75],"two":[76],"trained":[80,214],"VGG16":[81],"ConvNeXt":[83],"classification":[84,152,160],"AD":[87,128,167],"classification.":[88],"first":[90],"was":[93,107],"Denoising":[95],"Diffusion":[96],"Probabilistic":[97],"Model":[98],"(DDPM)":[99],"with":[100,116],"U-Net":[102],"architecture,":[103],"second":[106],"U-KAN":[109],"framework":[110],"that":[111],"integrates":[112],"Kolmogorov-Arnold":[113],"Networks":[114],"(KANs)":[115],"UNet.":[118],"Both":[119],"were":[121,169,191],"fine-tuned":[122],"generate":[124],"MRI":[125],"scans":[126],"or":[129],"Late":[130],"Mild":[131],"Cognitive":[132],"Impairment":[133],"(LMCI).":[134],"We":[135],"conducted":[136],"comparative":[138],"analysis":[139],"assess":[141],"reliability":[143],"usefulness":[145],"these":[147],"models.":[153],"best":[155,189],"metrics":[156],"achieved":[157],"using":[162],"class":[168],"precision":[170,192],"96%,":[172],"recall":[173,195],"83%,":[175],"F1":[176,198],"score":[177,199],"87%,":[179],"AUC":[181,203],"0.88.":[183,205],"For":[184],"LMCI":[186],"class,":[187],"values":[190],"78%,":[194],"88%,":[197],"82%,":[201],"They":[206],"both":[207],"demonstrated":[208],"noticeable":[209],"improvement":[210],"baseline":[213],"only":[215],"on":[216],"original":[218]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-01-21T00:00:00"}