{"id":"https://openalex.org/W7135232533","doi":"https://doi.org/10.48550/arxiv.2603.11818","title":"Automated Detection of Malignant Lesions in the Ovary Using Deep Learning Models and XAI","display_name":"Automated Detection of Malignant Lesions in the Ovary Using Deep Learning Models and XAI","publication_year":2026,"publication_date":"2026-03-12","ids":{"openalex":"https://openalex.org/W7135232533","doi":"https://doi.org/10.48550/arxiv.2603.11818"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.11818","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11818","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.11818","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ifty, Md. Hasin Sarwar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ifty, Md. Hasin Sarwar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Nirjan, Nisharga","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nirjan, Nisharga","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Islam, Labib","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Islam, Labib","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Diganta, M. A.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Diganta, M. A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Ornate, Reeyad Ahmed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ornate, Reeyad Ahmed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Tasnim, Anika","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tasnim, Anika","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Islam, Md. Saiful","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Islam, Md. Saiful","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.5202000141143799,"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":0.5202000141143799,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.13840000331401825,"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/T10550","display_name":"Ovarian cancer diagnosis and treatment","score":0.11540000140666962,"subfield":{"id":"https://openalex.org/subfields/2743","display_name":"Reproductive Medicine"},"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/deep-learning","display_name":"Deep learning","score":0.7540000081062317},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6847000122070312},{"id":"https://openalex.org/keywords/radiomics","display_name":"Radiomics","score":0.5519999861717224},{"id":"https://openalex.org/keywords/ovarian-cancer","display_name":"Ovarian cancer","score":0.42739999294281006},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3635999858379364},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3443000018596649},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.34279999136924744},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.34220001101493835}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7767000198364258},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7540000081062317},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7368000149726868},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6847000122070312},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6003000140190125},{"id":"https://openalex.org/C2778559731","wikidata":"https://www.wikidata.org/wiki/Q23808793","display_name":"Radiomics","level":2,"score":0.5519999861717224},{"id":"https://openalex.org/C2780427987","wikidata":"https://www.wikidata.org/wiki/Q172341","display_name":"Ovarian cancer","level":3,"score":0.42739999294281006},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3635999858379364},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3443000018596649},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C3020225094","wikidata":"https://www.wikidata.org/wiki/Q80091","display_name":"Area under curve","level":3,"score":0.3140999972820282},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2754000127315521},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2721000015735626},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.25839999318122864},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.11818","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11818","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.11818","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11818","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.5347391366958618,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"unrestrained":[1],"proliferation":[2],"of":[3,45,146,154,191,221,227],"cells":[4],"that":[5,103,174,224],"are":[6,17,47],"malignant":[7],"in":[8,78,198,232],"nature":[9],"is":[10,218],"cancer.":[11,109,240],"In":[12],"recent":[13],"times,":[14],"medical":[15,32],"professionals":[16],"constantly":[18],"acquiring":[19],"enhanced":[20],"diagnostic":[21],"and":[22,40,49,68,90,99,106,138,163],"treatment":[23],"abilities":[24],"by":[25,51],"implementing":[26],"deep":[27],"learning":[28],"models":[29,132],"to":[30,95,140],"analyze":[31],"data":[33],"for":[34,74,203,212,238],"better":[35,235],"clinical":[36],"decision,":[37],"disease":[38],"diagnosis":[39],"drug":[41],"discovery.":[42],"A":[43],"majority":[44],"cancers":[46],"studied":[48],"treated":[50],"incorporating":[52],"these":[53],"technologies.":[54],"However,":[55],"ovarian":[56,108,239],"cancer":[57],"remains":[58],"a":[59,69,101,124,234],"dilemma":[60],"as":[61,86,134],"it":[62,171],"has":[63,119],"inaccurate":[64],"non-invasive":[65],"detection":[66,236],"procedures":[67],"time":[70],"consuming,":[71],"invasive":[72],"procedure":[73],"accurate":[75],"detection.":[76],"Thus,":[77],"this":[79,222],"research,":[80],"several":[81],"Convolutional":[82],"Neural":[83],"Networks":[84],"such":[85,133],"LeNet-5,":[87],"ResNet,":[88],"VGGNet":[89],"GoogLeNet/Inception":[91],"have":[92,165,209],"been":[93,120,166,210],"utilized":[94,127],"develop":[96],"15":[97],"variants":[98],"choose":[100],"model":[102,112,179],"accurately":[104],"detects":[105],"identifies":[107],"For":[110,150],"effective":[111],"training,":[113],"the":[114,142,147,152,155,169,175,183,195,199,205,219,225,228],"dataset":[115],"OvarianCancer&SubtypesDatasetHistopathology":[116],"from":[117],"Mendeley":[118],"used.":[121,167],"After":[122],"constructing":[123],"model,":[125,156],"we":[126],"Explainable":[128],"Artificial":[129],"Intelligence":[130],"(XAI)":[131],"LIME,":[135],"Integrated":[136],"Gradients":[137],"SHAP":[139],"explain":[141],"black":[143],"box":[144],"outcome":[145],"selected":[148],"model.":[149],"evaluating":[151],"performance":[153,196],"Accuracy,":[157],"Precision,":[158],"Recall,":[159],"F1-Score,":[160],"ROC":[161],"Curve":[162],"AUC":[164],"From":[168],"evaluation,":[170],"was":[172],"seen":[173],"slightly":[176],"compact":[177],"InceptionV3":[178],"with":[180],"ReLu":[181],"had":[182],"overall":[184,214],"best":[185],"result":[186],"achieving":[187,233],"an":[188,213],"average":[189],"score":[190],"94%":[192],"across":[193],"all":[194],"metrics":[197],"augmented":[200],"dataset.":[201],"Lastly":[202],"XAI,":[204],"three":[206],"aforementioned":[207],"XAI":[208],"used":[211],"comparative":[215],"analysis.":[216],"It":[217],"aim":[220],"research":[223],"contributions":[226],"study":[229],"will":[230],"help":[231],"method":[237]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}