{"id":"https://openalex.org/W4410057655","doi":"https://doi.org/10.32604/cmc.2025.063634","title":"An Advanced Medical Diagnosis of Breast Cancer Histopathology Using Convolutional Neural Networks","display_name":"An Advanced Medical Diagnosis of Breast Cancer Histopathology Using Convolutional Neural Networks","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4410057655","doi":"https://doi.org/10.32604/cmc.2025.063634"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.063634","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.063634","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.063634","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057983090","display_name":"Ahmed Ben Atitallah","orcid":"https://orcid.org/0000-0002-2121-4417"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ahmed Ben Atitallah","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117422794","display_name":"Jannet Kamoun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jannet Kamoun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082492641","display_name":"Meshari D. Alanazi","orcid":"https://orcid.org/0000-0003-1483-6207"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meshari D. Alanazi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053868880","display_name":"Turki M. Alanazi","orcid":"https://orcid.org/0000-0002-1314-7146"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Turki M. Alanazi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035974933","display_name":"Mohammed Albekairi","orcid":"https://orcid.org/0000-0002-5165-5950"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammed Albekairi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5029354899","display_name":"Khaled Ka\u00e2niche","orcid":"https://orcid.org/0000-0003-0625-6245"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khaled Kaaniche","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5057983090"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.8076,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.90491123,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"83","issue":"3","first_page":"5761","last_page":"5779"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9412999749183655,"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":0.9412999749183655,"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/histopathology","display_name":"Histopathology","score":0.8450244665145874},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6546511054039001},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.5961208939552307},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.469511479139328},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.4003932774066925},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3462964594364166},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.3134704828262329},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3055785298347473},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.25043001770973206}],"concepts":[{"id":"https://openalex.org/C544855455","wikidata":"https://www.wikidata.org/wiki/Q1070952","display_name":"Histopathology","level":2,"score":0.8450244665145874},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6546511054039001},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.5961208939552307},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.469511479139328},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.4003932774066925},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3462964594364166},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.3134704828262329},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3055785298347473},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.25043001770973206}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.063634","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.063634","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.063634","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.063634","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2160374037","https://openalex.org/W2166764952","https://openalex.org/W2344480160","https://openalex.org/W2883935556","https://openalex.org/W2977153525","https://openalex.org/W2983999017","https://openalex.org/W3039059613","https://openalex.org/W3081268292","https://openalex.org/W3107669607","https://openalex.org/W3203189213","https://openalex.org/W3209220738","https://openalex.org/W4210850799","https://openalex.org/W4221053294","https://openalex.org/W4225086334","https://openalex.org/W4298087619","https://openalex.org/W4318672081","https://openalex.org/W4321165674","https://openalex.org/W4376137927","https://openalex.org/W4391353444","https://openalex.org/W4394751806","https://openalex.org/W4396795355","https://openalex.org/W4404455385"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W3039419443","https://openalex.org/W4386772532","https://openalex.org/W4393212117","https://openalex.org/W2115661411","https://openalex.org/W2399391471","https://openalex.org/W2400254106","https://openalex.org/W2970729894","https://openalex.org/W4381996710","https://openalex.org/W4294739205"],"abstract_inverted_index":{"Breast":[0],"Cancer":[1],"(BC)":[2],"remains":[3],"a":[4,67,92,149],"leading":[5],"malignancy":[6],"among":[7],"women,":[8],"resulting":[9],"in":[10,188,222],"high":[11],"mortality":[12],"rates.":[13,213],"Early":[14],"and":[15,35,75,91,100,114,131,142,167,199,210,227],"accurate":[16],"detection":[17],"is":[18],"crucial":[19],"for":[20,97,231],"improving":[21,207],"patient":[22,211,237],"outcomes.":[23],"Traditional":[24],"diagnostic":[25,50,189,204,233],"tools,":[26],"while":[27],"effective,":[28],"have":[29],"limitations":[30],"that":[31],"reduce":[32,193],"their":[33],"accessibility":[34],"accuracy.":[36],"This":[37,214],"study":[38,215],"investigates":[39],"the":[40,49,56,73,108,176,201,217,229],"use":[41],"of":[42,52,62,77,116,152,157,186,203,219],"Convolutional":[43],"Neural":[44],"Networks":[45],"(CNNs)":[46],"to":[47,71,106,181,235],"enhance":[48],"process":[51],"BC":[53,208,224],"histopathology.":[54],"Utilizing":[55],"BreakHis":[57],"dataset,":[58],"which":[59],"contains":[60],"thousands":[61],"histopathological":[63,225],"images,":[64],"we":[65],"developed":[66],"CNN":[68,81,146],"model":[69,120,147],"designed":[70,84],"improve":[72,236],"speed":[74],"accuracy":[76,151],"image":[78],"analysis.":[79],"Our":[80,145],"architecture":[82],"was":[83,104,124],"with":[85,135,154],"multiple":[86],"convolutional":[87],"layers,":[88,90],"max-pooling":[89],"fully":[93],"connected":[94],"network":[95],"optimized":[96],"feature":[98],"extraction":[99],"classification.":[101],"Hyperparameter":[102],"tuning":[103],"conducted":[105],"identify":[107],"optimal":[109],"learning":[110,221],"rate,":[111],"batch":[112],"size,":[113],"number":[115],"epochs,":[117],"ensuring":[118],"robust":[119],"performance.":[121],"The":[122,184],"dataset":[123],"divided":[125],"into":[126],"training":[127],"(80%),":[128],"validation":[129],"(10%),":[130],"testing":[132],"(10%)":[133],"subsets,":[134],"performance":[136,179],"evaluated":[137],"using":[138],"accuracy,":[139],"precision,":[140],"recall,":[141],"F1-score":[143],"metrics.":[144],"achieved":[148],"magnification-independent":[150],"97.72%,":[153],"specific":[155],"accuracies":[156],"97.50%":[158],"at":[159,162,165,169],"40\u00d7,":[160],"97.61%":[161],"100\u00d7,":[163],"99.06%":[164],"200\u00d7,":[166],"97.25%":[168],"400\u00d7":[170],"magnification":[171],"levels.":[172],"These":[173],"results":[174],"demonstrate":[175],"model\u2019s":[177],"superior":[178],"relative":[180],"existing":[182],"methods.":[183],"integration":[185],"CNNs":[187],"workflows":[190],"can":[191],"potentially":[192],"pathologist":[194],"workload,":[195],"minimize":[196],"interpretation":[197],"errors,":[198],"increase":[200],"availability":[202],"testing,":[205],"thereby":[206],"management":[209],"survival":[212],"highlights":[216],"effectiveness":[218],"deep":[220],"automating":[223],"classification":[226],"underscores":[228],"potential":[230],"AI-driven":[232],"solutions":[234],"care.":[238]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}