{"id":"https://openalex.org/W4388915754","doi":"https://doi.org/10.1109/wincom59760.2023.10322998","title":"Enhancing Breast Masses Detection and Segmentation: A Novel U-Net-Based Approach","display_name":"Enhancing Breast Masses Detection and Segmentation: A Novel U-Net-Based Approach","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4388915754","doi":"https://doi.org/10.1109/wincom59760.2023.10322998"},"language":"en","primary_location":{"id":"doi:10.1109/wincom59760.2023.10322998","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wincom59760.2023.10322998","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 10th International Conference on Wireless Networks and Mobile Communications (WINCOM)","raw_type":"proceedings-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/A5093318876","display_name":"Noura Bentaher","orcid":"https://orcid.org/0009-0007-5967-2564"},"institutions":[{"id":"https://openalex.org/I4210088687","display_name":"Universit\u00e9 Ibn Zohr","ror":"https://ror.org/006sgpv47","country_code":"MA","type":"education","lineage":["https://openalex.org/I4210088687"]}],"countries":["MA"],"is_corresponding":true,"raw_author_name":"Noura Bentaher","raw_affiliation_strings":["Ibnou Zohr University,LabSIV,Dept. of Computer Science Faculty of Science,Agadir,Morocco","Dept. of Computer Science Faculty of Science, LabSIV, Ibnou Zohr University, Agadir, Morocco"],"affiliations":[{"raw_affiliation_string":"Ibnou Zohr University,LabSIV,Dept. of Computer Science Faculty of Science,Agadir,Morocco","institution_ids":["https://openalex.org/I4210088687"]},{"raw_affiliation_string":"Dept. of Computer Science Faculty of Science, LabSIV, Ibnou Zohr University, Agadir, Morocco","institution_ids":["https://openalex.org/I4210088687"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083472526","display_name":"Younes Kabbadj","orcid":null},"institutions":[{"id":"https://openalex.org/I4210088687","display_name":"Universit\u00e9 Ibn Zohr","ror":"https://ror.org/006sgpv47","country_code":"MA","type":"education","lineage":["https://openalex.org/I4210088687"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Younes Kabbadj","raw_affiliation_strings":["Ibnou Zohr University,LabSIV,Dept. of Computer Science Faculty of Science,Agadir,Morocco","Dept. of Computer Science Faculty of Science, LabSIV, Ibnou Zohr University, Agadir, Morocco"],"affiliations":[{"raw_affiliation_string":"Ibnou Zohr University,LabSIV,Dept. of Computer Science Faculty of Science,Agadir,Morocco","institution_ids":["https://openalex.org/I4210088687"]},{"raw_affiliation_string":"Dept. of Computer Science Faculty of Science, LabSIV, Ibnou Zohr University, Agadir, Morocco","institution_ids":["https://openalex.org/I4210088687"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006383641","display_name":"Mohamed Ben Salah","orcid":"https://orcid.org/0000-0001-6847-6361"},"institutions":[{"id":"https://openalex.org/I4210088687","display_name":"Universit\u00e9 Ibn Zohr","ror":"https://ror.org/006sgpv47","country_code":"MA","type":"education","lineage":["https://openalex.org/I4210088687"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Mohamed Ben Salah","raw_affiliation_strings":["Ibnou Zohr University,LabSIV,Dept. of Computer Science Faculty of Science,Agadir,Morocco","Dept. of Computer Science Faculty of Science, LabSIV, Ibnou Zohr University, Agadir, Morocco"],"affiliations":[{"raw_affiliation_string":"Ibnou Zohr University,LabSIV,Dept. of Computer Science Faculty of Science,Agadir,Morocco","institution_ids":["https://openalex.org/I4210088687"]},{"raw_affiliation_string":"Dept. of Computer Science Faculty of Science, LabSIV, Ibnou Zohr University, Agadir, Morocco","institution_ids":["https://openalex.org/I4210088687"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5093318876"],"corresponding_institution_ids":["https://openalex.org/I4210088687"],"apc_list":null,"apc_paid":null,"fwci":0.3497,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66764718,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9890000224113464,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9883000254631042,"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/computer-science","display_name":"Computer science","score":0.8154305219650269},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7294061183929443},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.697512149810791},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6888430118560791},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6632147431373596},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.6308897137641907},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5515282154083252},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5379799604415894},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.4910562038421631},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44744497537612915},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4106810390949249},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.17662984132766724}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8154305219650269},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7294061183929443},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.697512149810791},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6888430118560791},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6632147431373596},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.6308897137641907},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5515282154083252},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5379799604415894},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.4910562038421631},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44744497537612915},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4106810390949249},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.17662984132766724},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wincom59760.2023.10322998","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wincom59760.2023.10322998","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 10th International Conference on Wireless Networks and Mobile Communications (WINCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.8700000047683716,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1993947467","https://openalex.org/W2597609879","https://openalex.org/W2774980628","https://openalex.org/W2944539652","https://openalex.org/W2962858109","https://openalex.org/W2986358226","https://openalex.org/W3010322859","https://openalex.org/W3012612365","https://openalex.org/W3100758200","https://openalex.org/W3118916726","https://openalex.org/W3120897110","https://openalex.org/W3132455321","https://openalex.org/W3196037238","https://openalex.org/W4214630905","https://openalex.org/W4225531241","https://openalex.org/W4323362423","https://openalex.org/W4382193078"],"related_works":["https://openalex.org/W1557094818","https://openalex.org/W2183246718","https://openalex.org/W2099261052","https://openalex.org/W3209204065","https://openalex.org/W4293226380","https://openalex.org/W2105707930","https://openalex.org/W1755711892","https://openalex.org/W2160907113","https://openalex.org/W1514924336","https://openalex.org/W2070813941"],"abstract_inverted_index":{"Breast":[0],"cancer":[1,7,92,185,257],"is":[2,13,21,56,72,218],"a":[3,14,57,63,87,102,166,175,189,252],"frequent":[4],"form":[5],"of":[6,17,173,183,214,223,239,276,304],"for":[8,23,74,90,106,125],"women,":[9],"and":[10,26,28,53,62,94,117,129,139,195,235,281,299],"globally":[11],"it":[12,71],"major":[15],"cause":[16],"death.":[18],"Early":[19],"detection":[20,93,186,194],"crucial":[22],"effective":[24],"diagnosis":[25],"treatment,":[27],"can":[29,232],"also":[30],"help":[31],"to":[32,45,47,77,85,100,164,180,219,266,279,287,294,300],"lower":[33],"mortality":[34],"rates.":[35],"In":[36,285],"recent":[37],"years,":[38],"Convolutional":[39],"Neural":[40],"Networks":[41],"have":[42,301],"been":[43],"applied":[44],"mammography":[46,110,201,277],"assist":[48],"radiologists":[49],"improve":[50],"their":[51],"efficiency":[52],"precision.":[54],"U-Net":[55],"fully":[58],"convolutional":[59],"neural":[60],"network":[61],"popular":[64],"model":[65,89,105,146,156,261],"that":[66,147,192,206,288],"uses":[67],"the":[68,114,144,159,181,199,215,221,237,240,273,296,305],"encoder-decoder":[69],"architecture,":[70],"known":[73],"its":[75],"capacity":[76],"effectively":[78],"segment":[79],"biomedical":[80],"images.":[81],"This":[82],"work":[83],"aims":[84],"present":[86],"new":[88],"breast":[91,107,184,224,256],"segmentation.":[95],"Our":[96,177,259],"first":[97],"purpose":[98],"was":[99],"discover":[101],"reliable":[103],"segmentation":[104,145,196,226],"masses":[108,225],"in":[109,127,162,255,292],"images":[111,278],"by":[112,157,187,227],"combining":[113],"ResNet":[115],"architecture":[116],"U-Net.":[118],"Subsequently,":[119],"we":[120,152,289],"trained":[121],"several":[122],"standard":[123],"models":[124],"classification":[126,155,260,269],"normal":[128],"abnormal":[130],"images,":[131],"such":[132],"as":[133],"ResNet101,":[134],"ResNet152,":[135],"DenseNet169,":[136],"MobileNet,":[137],"InceptionV3,":[138],"NasNet.":[140],"Furthermore,":[141,242],"based":[142],"on":[143,198,208,246],"yielded":[148],"good":[149],"experimental":[150],"results,":[151],"created":[153],"our":[154,243,283],"modifying":[158],"output":[160],"layers":[161],"order":[163,293],"generate":[165],"prediction":[167,297],"label":[168],"(Normal":[169],"or":[170],"Abnormal)":[171],"instead":[172],"generating":[174],"mask.":[176],"study":[178],"contributes":[179],"field":[182],"employing":[188],"novel":[190],"approach":[191,217,244],"performs":[193],"directly":[197],"entire":[200],"image,":[202],"unlike":[203],"traditional":[204],"methods":[205],"operate":[207],"image":[209],"patches.":[210],"The":[211],"main":[212],"advantage":[213],"proposed":[216],"simplify":[220],"workflow":[222],"eliminating":[228],"intermediate":[229],"steps":[230],"which":[231,250],"introduce":[233],"errors":[234],"increase":[236],"complexity":[238],"system.":[241],"focuses":[245],"reducing":[247],"false":[248],"positives,":[249],"are":[251],"common":[253],"challenge":[254],"detection.":[258],"reached":[262],"better":[263],"results":[264,298],"compared":[265],"other":[267],"experimented":[268],"models.":[270,284],"We":[271],"used":[272],"INbreast":[274],"dataset":[275],"train":[280],"evaluate":[282],"addition":[286],"employed":[290],"GradCAM":[291],"visualize":[295],"an":[302],"interpretation":[303],"results.":[306]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}