{"id":"https://openalex.org/W4389332250","doi":"https://doi.org/10.1109/cce60043.2023.10332862","title":"Classification of breast ultrasound images in BI-RADS categories using binary decomposition strategies with convolutional neural networks","display_name":"Classification of breast ultrasound images in BI-RADS categories using binary decomposition strategies with convolutional neural networks","publication_year":2023,"publication_date":"2023-10-25","ids":{"openalex":"https://openalex.org/W4389332250","doi":"https://doi.org/10.1109/cce60043.2023.10332862"},"language":"en","primary_location":{"id":"doi:10.1109/cce60043.2023.10332862","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cce60043.2023.10332862","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 20th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","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/A5092812241","display_name":"Juan Abdiel S\u00e1enz-S\u00e1nchez","orcid":null},"institutions":[{"id":"https://openalex.org/I68368234","display_name":"Center for Research and Advanced Studies of the National Polytechnic Institute","ror":"https://ror.org/009eqmr18","country_code":"MX","type":"facility","lineage":["https://openalex.org/I59361560","https://openalex.org/I68368234"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Juan Abdiel S\u00e1enz-S\u00e1nchez","raw_affiliation_strings":["Cinvestav, Unidad Tamaulipas,Mexico","Cinvestav, Unidad Tamaulipas, Mexico"],"affiliations":[{"raw_affiliation_string":"Cinvestav, Unidad Tamaulipas,Mexico","institution_ids":["https://openalex.org/I68368234"]},{"raw_affiliation_string":"Cinvestav, Unidad Tamaulipas, Mexico","institution_ids":["https://openalex.org/I68368234"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055336473","display_name":"Wilfrido G\u00f3mez\u2010Flores","orcid":"https://orcid.org/0000-0001-6758-6155"},"institutions":[{"id":"https://openalex.org/I68368234","display_name":"Center for Research and Advanced Studies of the National Polytechnic Institute","ror":"https://ror.org/009eqmr18","country_code":"MX","type":"facility","lineage":["https://openalex.org/I59361560","https://openalex.org/I68368234"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Wilfrido G\u00f3mez-Flores","raw_affiliation_strings":["Cinvestav, Unidad Tamaulipas,Mexico","Cinvestav, Unidad Tamaulipas, Mexico"],"affiliations":[{"raw_affiliation_string":"Cinvestav, Unidad Tamaulipas,Mexico","institution_ids":["https://openalex.org/I68368234"]},{"raw_affiliation_string":"Cinvestav, Unidad Tamaulipas, Mexico","institution_ids":["https://openalex.org/I68368234"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5092812241"],"corresponding_institution_ids":["https://openalex.org/I68368234"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16097788,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","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":0.998199999332428,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9707000255584717,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7910186052322388},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"Multiclass classification","score":0.7495222091674805},{"id":"https://openalex.org/keywords/bi-rads","display_name":"BI-RADS","score":0.6989334225654602},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6594987511634827},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6535094380378723},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6287050843238831},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.6021159291267395},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5595641136169434},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5415096879005432},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4348507821559906},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2544398605823517},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18145477771759033},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.15554288029670715},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.15103355050086975},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.12354466319084167},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.10322993993759155}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7910186052322388},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.7495222091674805},{"id":"https://openalex.org/C2779098232","wikidata":"https://www.wikidata.org/wiki/Q903975","display_name":"BI-RADS","level":5,"score":0.6989334225654602},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6594987511634827},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6535094380378723},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6287050843238831},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.6021159291267395},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5595641136169434},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5415096879005432},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4348507821559906},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2544398605823517},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18145477771759033},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.15554288029670715},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.15103355050086975},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.12354466319084167},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.10322993993759155},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cce60043.2023.10332862","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cce60043.2023.10332862","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 20th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2019575783","https://openalex.org/W2146615496","https://openalex.org/W2149523789","https://openalex.org/W2170505850","https://openalex.org/W2492794003","https://openalex.org/W2531409750","https://openalex.org/W2913668833","https://openalex.org/W2922358453","https://openalex.org/W2929215516","https://openalex.org/W2954996726","https://openalex.org/W3012253355","https://openalex.org/W3128646645","https://openalex.org/W4206967425","https://openalex.org/W4211107679","https://openalex.org/W4213190131","https://openalex.org/W4288064372","https://openalex.org/W4295923226","https://openalex.org/W4386758139","https://openalex.org/W6600213771","https://openalex.org/W6683380652"],"related_works":["https://openalex.org/W2952813363","https://openalex.org/W4376528628","https://openalex.org/W4360783045","https://openalex.org/W2963346891","https://openalex.org/W3176438653","https://openalex.org/W3202613528","https://openalex.org/W2770149305","https://openalex.org/W1537592868","https://openalex.org/W2470590370","https://openalex.org/W2910954186"],"abstract_inverted_index":{"The":[0,160],"Breast":[1],"Imaging":[2],"Reporting":[3],"&":[4],"Data":[5],"System":[6],"(BI-RADS)":[7],"assesses":[8],"breast":[9,116],"findings":[10],"in":[11,149,157],"terms":[12],"of":[13,15,17,24,180,196],"categories":[14,30,45,144,153],"risk":[16],"malignancy":[18],"from":[19],"which":[20],"the":[21,60,129,164,168,210],"clinical":[22],"conduct":[23],"patients":[25],"is":[26,133],"determined.":[27],"In":[28,43],"particular,":[29],"2":[31],"and":[32,47,125,147,152,155,174,194,198,206],"3":[33],"are":[34,49,142],"related":[35],"to":[36,58,83,115],"benign":[37,150],"tumors,":[38],"recommending":[39],"routine":[40],"imaging":[41],"studies.":[42],"contrast,":[44],"4":[46,148,154],"5":[48,156],"associated":[50],"with":[51,176],"suspicious":[52],"tumors":[53,151],"analyzed":[54],"by":[55,70,93],"biopsy":[56],"exam":[57],"determine":[59],"pathology.":[61],"Recently,":[62],"this":[63],"multiclass":[64,88,101,211],"classification":[65,89,170,212,217],"problem":[66,102,213],"has":[67],"been":[68],"addressed":[69],"a":[71,80,100,136,177,183,192,201],"single":[72,184,202],"convolutional":[73],"neural":[74],"network":[75],"(CNN)":[76],"model":[77,132,186],"that":[78,98,163],"provides":[79],"posterior":[81],"probability":[82],"each":[84],"BI-RADS":[85,140],"category.":[86],"Nevertheless,":[87],"can":[90],"be":[91],"improved":[92],"using":[94],"binary":[95,111,216],"decomposition":[96,112],"strategies":[97,113],"map":[99],"on":[103],"several":[104],"two-class":[105],"problems.":[106],"This":[107],"paper":[108],"compares":[109],"three":[110],"applied":[114],"ultrasound":[117],"(BUS)":[118],"image":[119],"classification:":[120],"one-versus-all":[121],"(OVA),":[122],"one-versus-one":[123],"(OVO),":[124],"all-and-one":[126],"(A&O),":[127],"where":[128],"CNN":[130,185,203],"Xception":[131],"used":[134],"as":[135],"base":[137],"learner.":[138],"Five":[139],"classes":[141],"considered:":[143],"2,":[145],"3,":[146],"malignant":[158],"tumors.":[159],"results":[161],"show":[162],"OVA":[165,190],"strategy":[166],"presents":[167],"best":[169],"performance":[171],"regarding":[172],"OVO":[173],"A&O":[175],"general":[178],"accuracy":[179],"60%,":[181],"while":[182,200],"reaches":[187],"57%.":[188],"Besides,":[189],"obtains":[191],"sensitivity":[193],"specificity":[195],"0.80":[197],"0.93,":[199],"attains":[204],"0.77":[205],"0.94.":[207],"Hence,":[208],"dividing":[209],"into":[214],"simpler":[215],"sub-problems":[218],"improves":[219],"BUS":[220],"classification.":[221]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}
