{"id":"https://openalex.org/W4306754912","doi":"https://doi.org/10.1108/dta-05-2022-0210","title":"A new approach for histological classification of breast cancer using deep hybrid heterogenous ensemble","display_name":"A new approach for histological classification of breast cancer using deep hybrid heterogenous ensemble","publication_year":2022,"publication_date":"2022-10-18","ids":{"openalex":"https://openalex.org/W4306754912","doi":"https://doi.org/10.1108/dta-05-2022-0210"},"language":"en","primary_location":{"id":"doi:10.1108/dta-05-2022-0210","is_oa":false,"landing_page_url":"https://doi.org/10.1108/dta-05-2022-0210","pdf_url":null,"source":{"id":"https://openalex.org/S4210171756","display_name":"Data Technologies and Applications","issn_l":"2514-9288","issn":["2514-9288","2514-9318"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Technologies and Applications","raw_type":"journal-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/A5012997021","display_name":"Hasnae Zerouaoui","orcid":"https://orcid.org/0000-0001-7268-8404"},"institutions":[{"id":"https://openalex.org/I4210131560","display_name":"Universit\u00e9 Mohammed VI Polytechnique","ror":"https://ror.org/03xc55g68","country_code":"MA","type":"education","lineage":["https://openalex.org/I4210131560"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Hasnae Zerouaoui","raw_affiliation_strings":["MSDA, Mohammed VI Polytechnic University, Ben Guerir, Morocco"],"raw_orcid":"https://orcid.org/0000-0001-7268-8404","affiliations":[{"raw_affiliation_string":"MSDA, Mohammed VI Polytechnic University, Ben Guerir, Morocco","institution_ids":["https://openalex.org/I4210131560"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000218612","display_name":"Ali Idri","orcid":"https://orcid.org/0000-0002-4586-4158"},"institutions":[{"id":"https://openalex.org/I126477371","display_name":"Mohammed V University","ror":"https://ror.org/00r8w8f84","country_code":"MA","type":"education","lineage":["https://openalex.org/I126477371"]},{"id":"https://openalex.org/I4210131560","display_name":"Universit\u00e9 Mohammed VI Polytechnique","ror":"https://ror.org/03xc55g68","country_code":"MA","type":"education","lineage":["https://openalex.org/I4210131560"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Ali Idri","raw_affiliation_strings":["Modeling, Simulation and Data Analysis, Mohammed VI Polytechnic University, Ben Guerir, Morocco and Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco"],"raw_orcid":"https://orcid.org/0000-0002-4586-4158","affiliations":[{"raw_affiliation_string":"Modeling, Simulation and Data Analysis, Mohammed VI Polytechnic University, Ben Guerir, Morocco and Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco","institution_ids":["https://openalex.org/I4210131560","https://openalex.org/I126477371"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058118943","display_name":"Omar El Alaoui","orcid":"https://orcid.org/0000-0002-4395-9989"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Omar El Alaoui","raw_affiliation_strings":["UM5R ENSIAS, Rabat, Morocco"],"raw_orcid":"https://orcid.org/0000-0002-4395-9989","affiliations":[{"raw_affiliation_string":"UM5R ENSIAS, Rabat, Morocco","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6648,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.86552796,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"57","issue":"2","first_page":"245","last_page":"278"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":1.0,"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":1.0,"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":0.9977999925613403,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7085386514663696},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6715233325958252},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.631335973739624},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.602730393409729},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4727916419506073},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4691409468650818},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4441922605037689},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.40229833126068115},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3801248371601105}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7085386514663696},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6715233325958252},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.631335973739624},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.602730393409729},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4727916419506073},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4691409468650818},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4441922605037689},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.40229833126068115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3801248371601105}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/dta-05-2022-0210","is_oa":false,"landing_page_url":"https://doi.org/10.1108/dta-05-2022-0210","pdf_url":null,"source":{"id":"https://openalex.org/S4210171756","display_name":"Data Technologies and Applications","issn_l":"2514-9288","issn":["2514-9288","2514-9318"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Technologies and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8799999952316284,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1507763842","https://openalex.org/W1686810756","https://openalex.org/W1984425068","https://openalex.org/W2005504865","https://openalex.org/W2043841926","https://openalex.org/W2048207013","https://openalex.org/W2071698610","https://openalex.org/W2117539524","https://openalex.org/W2159002562","https://openalex.org/W2165698076","https://openalex.org/W2180559859","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2274287116","https://openalex.org/W2344480160","https://openalex.org/W2498647982","https://openalex.org/W2516246192","https://openalex.org/W2531409750","https://openalex.org/W2535021688","https://openalex.org/W2606095531","https://openalex.org/W2613680070","https://openalex.org/W2754995126","https://openalex.org/W2755012395","https://openalex.org/W2775795276","https://openalex.org/W2796438033","https://openalex.org/W2801540580","https://openalex.org/W2809348156","https://openalex.org/W2889646458","https://openalex.org/W2892942467","https://openalex.org/W2898280479","https://openalex.org/W2905371068","https://openalex.org/W2914762504","https://openalex.org/W2914774348","https://openalex.org/W2929968583","https://openalex.org/W2945819472","https://openalex.org/W2945839551","https://openalex.org/W2949306187","https://openalex.org/W2951468938","https://openalex.org/W2954665897","https://openalex.org/W2963446712","https://openalex.org/W2974974394","https://openalex.org/W2981322795","https://openalex.org/W2997538866","https://openalex.org/W3020203706","https://openalex.org/W3038784327","https://openalex.org/W3047083963","https://openalex.org/W3094910079","https://openalex.org/W3095595608","https://openalex.org/W3107669607","https://openalex.org/W3120006163","https://openalex.org/W3124608938","https://openalex.org/W3138634112","https://openalex.org/W3147142721","https://openalex.org/W3199579264","https://openalex.org/W3209220738","https://openalex.org/W4285114822","https://openalex.org/W4285174669"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W4375867731","https://openalex.org/W148178222","https://openalex.org/W2104657898","https://openalex.org/W2082756648","https://openalex.org/W1948992892","https://openalex.org/W1886884218","https://openalex.org/W1910826599","https://openalex.org/W1980100242","https://openalex.org/W4224922629"],"abstract_inverted_index":{"Purpose":[0],"Hundreds":[1],"of":[2,4,20,61,71,107,117,250],"thousands":[3],"deaths":[5],"each":[6],"year":[7],"in":[8,286],"the":[9,26,35,47,69,122,131,137,142,188,213,218,222,231,241,247,259,280,288,296],"world":[10],"are":[11],"caused":[12],"by":[13,31,41,105],"breast":[14],"cancer":[15],"(BC).":[16],"An":[17],"early-stage":[18],"diagnosis":[19,282],"this":[21],"disease":[22],"can":[23,276],"positively":[24],"reduce":[25],"morbidity":[27],"and":[28,54,85,91,102,109,157,165,183,185,200,205,216,240,245,254,266,291],"mortality":[29],"rate":[30],"helping":[32],"to":[33,130,135,140,211,221,283],"select":[34],"most":[36],"appropriate":[37],"treatment":[38],"options,":[39],"especially":[40],"using":[42,175],"histological":[43,115,189],"BC":[44,118,281],"images":[45],"for":[46,88,114,279,295],"diagnosis.":[48],"Design/methodology/approach":[49],"The":[50,146,270],"present":[51],"study":[52],"proposes":[53],"evaluates":[55],"a":[56],"novel":[57],"approach":[58],"which":[59],"consists":[60],"24":[62],"deep":[63,73,124,132,143,232,242,272],"hybrid":[64,125,233,273],"heterogenous":[65,126,234,274],"ensembles":[66,127,134,235,244,275],"that":[67,230],"combine":[68],"strength":[70],"seven":[72],"learning":[74],"techniques":[75,215,219],"(DenseNet":[76],"201,":[77],"Inception":[78],"V3,":[79],"VGG16,":[80],"VGG19,":[81],"Inception-ResNet-V3,":[82],"MobileNet":[83],"V2":[84],"ResNet":[86],"50)":[87],"feature":[89],"extraction":[90],"four":[92,150,176,194,260],"well-known":[93],"classifiers":[94],"(multi-layer":[95],"perceptron,":[96],"support":[97],"vector":[98],"machines,":[99],"K-nearest":[100],"neighbors":[101],"decision":[103],"tree)":[104],"means":[106],"hard":[108],"weighted":[110],"voting":[111,168],"combination":[112],"methods":[113],"classification":[116,151],"medical":[119],"image.":[120],"Furthermore,":[121],"best":[123,138,223],"were":[128,173,186,208],"compared":[129],"stacked":[133,243],"determine":[136],"strategy":[139],"design":[141],"ensemble":[144],"methods.":[145],"empirical":[147,171],"evaluations":[148,172],"used":[149,210],"performance":[152,177],"criteria":[153],"(accuracy,":[154],"sensitivity,":[155],"precision":[156],"F1-score),":[158],"fivefold":[159],"cross-validation,":[160],"Scott\u2013Knott":[161],"(SK)":[162],"statistical":[163,203],"test":[164,204],"Borda":[166,206],"count":[167,207],"method.":[169],"All":[170],"assessed":[174],"measures,":[178],"including":[179],"accuracy,":[180],"precision,":[181],"recall":[182],"F1-score,":[184],"over":[187],"BreakHis":[190],"public":[191],"dataset":[192],"with":[193],"magnification":[195,261],"factors":[196,262],"(40\u00d7,":[197],"100\u00d7,":[198,264],"200\u00d7":[199,265],"400\u00d7).":[201],"SK":[202,224],"also":[209],"cluster":[212],"designed":[214],"rank":[217],"belonging":[220],"cluster,":[225],"respectively.":[226,268],"Findings":[227],"Results":[228],"showed":[229],"outperformed":[236],"both":[237],"their":[238],"singles":[239],"reached":[246],"accuracy":[248],"values":[249],"96.3,":[251],"95.6,":[252],"96.3":[253],"94":[255],"per":[256],"cent":[257],"across":[258],"40\u00d7,":[263],"400\u00d7,":[267],"Originality/value":[269],"proposed":[271],"be":[277],"applied":[278],"assist":[284],"pathologists":[285],"reducing":[287],"missed":[289],"diagnoses":[290],"proposing":[292],"adequate":[293],"treatments":[294],"patients.":[297]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}