{"id":"https://openalex.org/W4405429674","doi":"https://doi.org/10.1109/access.2024.3518612","title":"Unleashing the Power of Hierarchical Variational Autoencoder for Predicting Breast Cancer","display_name":"Unleashing the Power of Hierarchical Variational Autoencoder for Predicting Breast Cancer","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4405429674","doi":"https://doi.org/10.1109/access.2024.3518612"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3518612","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3518612","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2024.3518612","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075026873","display_name":"V. Sreelekshmi","orcid":null},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"V. Sreelekshmi","raw_affiliation_strings":["Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, India"],"raw_orcid":"https://orcid.org/0009-0005-4936-7641","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, India","institution_ids":["https://openalex.org/I81556334"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035846372","display_name":"K Pavithran","orcid":"https://orcid.org/0000-0002-6129-5709"},"institutions":[{"id":"https://openalex.org/I1282879092","display_name":"Amrita Institute of Medical Sciences and Research Centre","ror":"https://ror.org/05ahcwz21","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I1282879092"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K. Pavithran","raw_affiliation_strings":["Department of Medical Oncology, Amrita Institute of Medical Sciences, Kochi, Kerala, India","Department of Medical Oncology, Amrita Institute of Medical Science, Kochi, Kerala, India"],"raw_orcid":"https://orcid.org/0000-0002-6129-5709","affiliations":[{"raw_affiliation_string":"Department of Medical Oncology, Amrita Institute of Medical Sciences, Kochi, Kerala, India","institution_ids":["https://openalex.org/I1282879092"]},{"raw_affiliation_string":"Department of Medical Oncology, Amrita Institute of Medical Science, Kochi, Kerala, India","institution_ids":["https://openalex.org/I1282879092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041549614","display_name":"Jyothisha J. Nair","orcid":"https://orcid.org/0000-0002-8050-8896"},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Jyothisha J. Nair","raw_affiliation_strings":["Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, India"],"raw_orcid":"https://orcid.org/0000-0002-8050-8896","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, India","institution_ids":["https://openalex.org/I81556334"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.7221,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.91750716,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"12","issue":null,"first_page":"195658","last_page":"195670"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9783999919891357,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9783999919891357,"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"}},{"id":"https://openalex.org/T12994","display_name":"Infrared Thermography in Medicine","score":0.9104999899864197,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T10885","display_name":"Gene expression and cancer classification","score":0.9049999713897705,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8420745134353638},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.680058479309082},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.5006365776062012},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4900554418563843},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3599618077278137},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.3157079815864563},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.19733160734176636},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17743390798568726},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.1061636209487915},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08506667613983154}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8420745134353638},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.680058479309082},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.5006365776062012},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4900554418563843},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3599618077278137},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.3157079815864563},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.19733160734176636},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17743390798568726},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.1061636209487915},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08506667613983154},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3518612","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3518612","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:75d2aa26daf14b789281392441e90057","is_oa":true,"landing_page_url":"https://doaj.org/article/75d2aa26daf14b789281392441e90057","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 195658-195670 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3518612","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3518612","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W37563857","https://openalex.org/W1713586625","https://openalex.org/W2102427741","https://openalex.org/W2145023731","https://openalex.org/W2150134853","https://openalex.org/W2165015795","https://openalex.org/W2271473780","https://openalex.org/W2341766664","https://openalex.org/W2518600128","https://openalex.org/W2785963404","https://openalex.org/W2787841985","https://openalex.org/W2944345944","https://openalex.org/W2996253120","https://openalex.org/W2998508940","https://openalex.org/W3196563098","https://openalex.org/W3197886029","https://openalex.org/W3200158435","https://openalex.org/W3210217201","https://openalex.org/W4200566850","https://openalex.org/W4220887299","https://openalex.org/W4281483376","https://openalex.org/W4283391351","https://openalex.org/W4308433738","https://openalex.org/W4313577028","https://openalex.org/W4322631230","https://openalex.org/W4390635891","https://openalex.org/W4392362001","https://openalex.org/W4394565318","https://openalex.org/W4396680671","https://openalex.org/W6682664657","https://openalex.org/W6739268145","https://openalex.org/W6790174191","https://openalex.org/W6804405508"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W4220775285"],"abstract_inverted_index":{"Breast":[0],"cancer":[1,89],"continues":[2],"to":[3,35,85],"be":[4],"a":[5,125,163,171,294],"major":[6],"health":[7],"concern":[8],"worldwide.":[9],"Early":[10],"and":[11,19,39,56,68,134,167,204,230,259,277,292],"accurate":[12],"prediction":[13],"is":[14,156],"crucial":[15],"for":[16,32,82,179,209],"effective":[17],"treatment":[18],"improving":[20],"survival":[21],"rates.":[22],"Computer":[23],"Aided":[24],"Diagnosis":[25],"system":[26],"serves":[27],"as":[28],"an":[29,78],"invaluable":[30],"tool":[31],"radiologists,":[33],"aiming":[34],"reduce":[36],"diagnostic":[37],"errors":[38],"enhance":[40],"the":[41,103,106,112,149,185,205,210,219,237,248,271,287],"accuracy":[42,186,214,227,243,256],"of":[43,105,131,187,215,221,224,228,234,244,250,253,257,263,273,289],"diagnosis.":[44,90],"These":[45],"systems":[46,285],"incorporate":[47],"various":[48],"processing":[49],"techniques,":[50],"including":[51],"pre-processing,":[52],"segmentation,":[53],"feature":[54,188],"extraction,":[55],"classification.":[57],"Moreover,":[58],"deep":[59],"learning":[60],"methods":[61,291],"frequently":[62],"suffer":[63],"from":[64],"sub":[65],"optimal":[66],"performance":[67],"demand":[69],"substantial":[70],"computational":[71],"resources.":[72],"This":[73],"study":[74],"focuses":[75],"on":[76,158],"developing":[77],"automated":[79],"classification":[80,193],"model":[81,93],"mammography":[83],"images":[84,110,143,161],"aid":[86],"in":[87,109,138,191,281,297],"breast":[88],"Our":[91,265],"proposed":[92],"initiates":[94],"with":[95],"noise":[96],"removal":[97,104],"using":[98,124,162,170],"median":[99],"filters,":[100],"followed":[101],"by":[102],"pectoral":[107],"muscle":[108],"through":[111],"Canny-edge":[113],"detection":[114],"method.":[115],"On":[116],"these":[117,159],"preprocessed":[118],"images,":[119],"we":[120],"applied":[121],"data":[122],"augmentation":[123],"two-point":[126],"crossover":[127],"technique,":[128],"addressing":[129],"issues":[130],"small":[132],"datasets":[133,201],"class":[135],"imbalances":[136],"common":[137],"medical":[139,298],"image":[140,299],"analysis.":[141],"The":[142,195],"then":[144,168],"undergo":[145],"multi-scale":[146,181],"representation":[147,182],"via":[148],"fourth-order":[150,176,274],"complex":[151,177,275],"diffusion":[152,178,276],"algorithm.":[153],"Feature":[154],"extraction":[155,189],"conducted":[157],"multi-scaled":[160],"Hierarchical":[164,278],"Variational":[165,279],"Auto-encoders":[166],"classified":[169],"Support":[172],"Vector":[173],"Machine.":[174],"Employing":[175],"initial":[180],"significantly":[183],"enhances":[184],"resulting":[190],"robust":[192],"performance.":[194],"training":[196],"process":[197],"involves":[198],"two":[199],"different":[200],"like":[202],"MIAS":[203,238],"KAU-BCMD.":[206],"Test":[207],"results":[208,241,267],"KAU-BCMD":[211],"dataset":[212],"include:":[213],"99.80%,":[216,229],"Area":[217,246],"Under":[218,247],"Curve":[220,249],"99.30%,":[222,245],"F1-score":[223,252],"99.20%,":[225],"balanced":[226,255],"Matthews":[231,260],"correlation":[232,261],"coefficient":[233,262],"99.20%.":[235],"For":[236],"dataset,":[239],"test":[240],"show":[242],"99.10%,":[251],"98.30%,":[254],"99.00%,":[258],"99.00%.":[264],"validation":[266],"clearly":[268],"indicate":[269],"that":[270],"integration":[272],"Auto-encoder":[280],"computer":[282],"aided":[283],"diagnosis":[284],"addresses":[286],"limitations":[288],"traditional":[290],"sets":[293],"new":[295],"benchmark":[296],"analysis,":[300],"ensuring":[301],"better":[302],"patient":[303],"outcomes.":[304]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":5}],"updated_date":"2026-06-17T08:01:34.144755","created_date":"2025-10-10T00:00:00"}
