{"id":"https://openalex.org/W4394564308","doi":"https://doi.org/10.1109/access.2024.3385011","title":"LMHistNet: Levenberg\u2013Marquardt Based Deep Neural Network for Classification of Breast Cancer Histopathological Images","display_name":"LMHistNet: Levenberg\u2013Marquardt Based Deep Neural Network for Classification of Breast Cancer Histopathological Images","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4394564308","doi":"https://doi.org/10.1109/access.2024.3385011"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3385011","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3385011","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10491266.pdf","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://ieeexplore.ieee.org/ielx7/6287639/6514899/10491266.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034660574","display_name":"Soumya Sara Koshy","orcid":"https://orcid.org/0000-0003-4476-614X"},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Soumya Sara Koshy","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India"],"raw_orcid":"https://orcid.org/0000-0003-4476-614X","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076924939","display_name":"L. Jani Anbarasi","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"L. Jani Anbarasi","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India"],"raw_orcid":"https://orcid.org/0000-0002-8904-2236","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":9.5809,"has_fulltext":true,"cited_by_count":40,"citation_normalized_percentile":{"value":0.98403811,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"12","issue":null,"first_page":"52051","last_page":"52066"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9962000250816345,"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":0.9962000250816345,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9423999786376953,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/levenberg\u2013marquardt-algorithm","display_name":"Levenberg\u2013Marquardt algorithm","score":0.966823160648346},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.6645883321762085},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6475564241409302},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6368627548217773},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.543071985244751},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4974682629108429},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43823644518852234},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4245506227016449},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.37559980154037476},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3085893392562866},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18669581413269043},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.0996558666229248}],"concepts":[{"id":"https://openalex.org/C87578567","wikidata":"https://www.wikidata.org/wiki/Q1426494","display_name":"Levenberg\u2013Marquardt algorithm","level":3,"score":0.966823160648346},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.6645883321762085},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6475564241409302},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6368627548217773},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.543071985244751},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4974682629108429},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43823644518852234},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4245506227016449},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.37559980154037476},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3085893392562866},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18669581413269043},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0996558666229248}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3385011","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3385011","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10491266.pdf","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:432b621337554a528ab70872d6f496c5","is_oa":true,"landing_page_url":"https://doaj.org/article/432b621337554a528ab70872d6f496c5","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 52051-52066 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3385011","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3385011","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10491266.pdf","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":[{"id":"https://metadata.un.org/sdg/5","score":0.4300000071525574,"display_name":"Gender equality"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4394564308.pdf","grobid_xml":"https://content.openalex.org/works/W4394564308.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W2151608510","https://openalex.org/W2183446018","https://openalex.org/W2248620004","https://openalex.org/W2344480160","https://openalex.org/W2570618306","https://openalex.org/W2591955246","https://openalex.org/W2598373920","https://openalex.org/W2609584387","https://openalex.org/W2620578070","https://openalex.org/W2716665989","https://openalex.org/W2770397194","https://openalex.org/W2794888513","https://openalex.org/W2801370692","https://openalex.org/W2807567209","https://openalex.org/W2883567318","https://openalex.org/W2889646458","https://openalex.org/W2900144270","https://openalex.org/W2900257566","https://openalex.org/W2903829201","https://openalex.org/W2912354537","https://openalex.org/W2915997673","https://openalex.org/W2952319621","https://openalex.org/W2973569693","https://openalex.org/W3006425969","https://openalex.org/W3009210879","https://openalex.org/W3033839164","https://openalex.org/W3035321842","https://openalex.org/W3093758045","https://openalex.org/W3107194210","https://openalex.org/W3121563664","https://openalex.org/W3134651670","https://openalex.org/W3172138850","https://openalex.org/W3197078391","https://openalex.org/W3202169414","https://openalex.org/W4200124967","https://openalex.org/W4205581135","https://openalex.org/W4206961833","https://openalex.org/W4207017358","https://openalex.org/W4213094779","https://openalex.org/W4220768105","https://openalex.org/W4251280792","https://openalex.org/W4282982312","https://openalex.org/W4288432579","https://openalex.org/W4289752563","https://openalex.org/W4289865919","https://openalex.org/W4294833989","https://openalex.org/W4297359647","https://openalex.org/W4306180875","https://openalex.org/W4307217876","https://openalex.org/W4379984415"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W1978671516","https://openalex.org/W2895947699","https://openalex.org/W2374738832","https://openalex.org/W3196668723","https://openalex.org/W2794551249","https://openalex.org/W2539062777","https://openalex.org/W2074154109","https://openalex.org/W4392064155","https://openalex.org/W2795259429"],"abstract_inverted_index":{"Breast":[0,23],"cancer":[1,16,24,83],"is":[2,17,26,76,96,119,130],"a":[3,20,27],"deadly":[4],"disease":[5],"commonly":[6],"affecting":[7],"women.":[8],"One":[9],"method":[10],"to":[11,18],"avoid":[12],"death":[13],"from":[14,32,152],"breast":[15,51,82,251],"obtain":[19],"diagnosis":[21],"early.":[22],"detection":[25],"significant":[28],"area":[29],"that":[30,200],"benefits":[31],"the":[33,45,79,110,123,167,176,182,191,201,208],"technological":[34],"advancements":[35],"in":[36],"artificial":[37],"intelligence.":[38],"A":[39,61],"deep":[40],"neural":[41,64],"network":[42,65],"architecture":[43],"for":[44,78,98,132,175,231,247],"classification":[46,80,233,249],"of":[47,50,81,158,163,170,250],"microscopic":[48],"images":[49,84,157,162,169],"tumor":[52,252],"tissue":[53],"acquired":[54],"using":[55,115,184],"excisional":[56],"biopsy":[57],"has":[58,104,186],"been":[59,105,187],"proposed.":[60],"hybrid":[62],"convolutional":[63],"model":[66,203],"with":[67],"asymmetric":[68],"convolutions":[69],"and":[70,141,166,193,214,220,228,241,256],"Levenberg\u2013Marquardt":[71],"optimization":[72],"named":[73],"as":[74,87,89,136,138],"LMHistNet":[75,185],"used":[77,131,174],"into":[85,234,254],"binary":[86,140,248],"well":[88,137],"eight":[90,142,235],"subclasses.":[91],"Convolution":[92],"block":[93],"attention":[94],"module":[95],"incorporated":[97],"adaptive":[99],"feature":[100],"refining.":[101],"Convergence":[102],"time":[103],"significantly":[106],"reduced":[107],"by":[108,121,148],"normalizing":[109],"input":[111],"features":[112,151],"on":[113,181,190],"models":[114],"batch":[116],"normalization.":[117],"Training":[118],"improved":[120],"reducing":[122],"internal":[124],"covariance":[125],"shift.":[126],"Hinge":[127],"loss":[128,192],"function":[129],"better":[133],"convergence.":[134],"Magnification-dependent":[135],"independent":[139],"class":[143],"classifications":[144],"are":[145,224,244],"efficiently":[146],"performed":[147],"extracting":[149],"diversified":[150],"histopathological":[153,156],"images.":[154],"7909":[155],"which":[159],"2480":[160],"were":[161,173],"benign":[164,255],"(normal)":[165],"remaining":[168],"malignant":[171,257],"patients":[172],"study.":[177],"Further,":[178],"performance":[179,206],"evaluation":[180],"dataset":[183],"analyzed":[188],"based":[189],"accuracy":[194],"curve.":[195],"The":[196,216,237],"experimental":[197],"findings":[198],"show":[199],"proposed":[202],"obtained":[204,223],"good":[205],"over":[207],"various":[209],"magnifications":[210],"40X,":[211],"100X,":[212],"200X":[213],"400X.":[215],"accuracy,":[217,238],"precision,":[218,239],"recall":[219,240],"F1":[221],"score":[222,243],"88,":[225],"89,":[226],"88":[227,229],"respectively":[230],"multiclass":[232],"subtypes.":[236],"f1":[242],"99":[245],"each":[246],"tissues":[253],"classes.":[258]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":7}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}
