{"id":"https://openalex.org/W4409209915","doi":"https://doi.org/10.32604/cmc.2025.063470","title":"UltraSegNet: A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images","display_name":"UltraSegNet: A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4409209915","doi":"https://doi.org/10.32604/cmc.2025.063470"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.063470","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.063470","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.2025.063470","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028604274","display_name":"Suhaila Abuowaida","orcid":"https://orcid.org/0000-0002-2030-4250"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Suhaila Abuowaida","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020013696","display_name":"Hamza Abu Owida","orcid":"https://orcid.org/0000-0001-6943-6134"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hamza Abu Owida","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106737050","display_name":"Deema Mohammed Alsekait","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deema Mohammed Alsekait","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024699080","display_name":"Nawaf Alshdaifat","orcid":"https://orcid.org/0000-0002-0542-1539"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nawaf Alshdaifat","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001630272","display_name":"Diaa Salama AbdElminaam","orcid":"https://orcid.org/0000-0002-1544-9906"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Diaa Salama AbdElminaam","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102843496","display_name":"Mohammad Alshinwan","orcid":"https://orcid.org/0000-0002-3864-7323"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammad Alshinwan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5028604274"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":20.4386,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.99159591,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"83","issue":"2","first_page":"3303","last_page":"3333"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9977999925613403,"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":0.9977999925613403,"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":0.9580000042915344,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.901199996471405,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6621931791305542},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6018614172935486},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.5815966725349426},{"id":"https://openalex.org/keywords/ultrasound","display_name":"Ultrasound","score":0.5627393126487732},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4713532328605652},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46028250455856323},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3840292692184448},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.29664891958236694},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.2965472340583801},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2866228222846985},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.1577371060848236}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6621931791305542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6018614172935486},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.5815966725349426},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.5627393126487732},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4713532328605652},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46028250455856323},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3840292692184448},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.29664891958236694},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.2965472340583801},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2866228222846985},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.1577371060848236}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.063470","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.063470","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.2025.063470","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.063470","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1980527762","https://openalex.org/W2010120422","https://openalex.org/W2020999234","https://openalex.org/W2028672930","https://openalex.org/W2038751842","https://openalex.org/W2049694710","https://openalex.org/W2096579040","https://openalex.org/W2104095591","https://openalex.org/W2118386984","https://openalex.org/W2119249988","https://openalex.org/W2124260943","https://openalex.org/W2132984323","https://openalex.org/W2142456468","https://openalex.org/W2145023731","https://openalex.org/W2149988868","https://openalex.org/W2156909104","https://openalex.org/W2529908594","https://openalex.org/W2533800772","https://openalex.org/W2592929672","https://openalex.org/W2744692634","https://openalex.org/W2911964244","https://openalex.org/W2991372685","https://openalex.org/W4212875960","https://openalex.org/W4319596565","https://openalex.org/W4379880150","https://openalex.org/W4389639038","https://openalex.org/W4395048328","https://openalex.org/W4396677248","https://openalex.org/W4400881081","https://openalex.org/W4402559718","https://openalex.org/W4402929483","https://openalex.org/W4406235561","https://openalex.org/W4406728218"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W4375867731","https://openalex.org/W2351153092","https://openalex.org/W2611989081","https://openalex.org/W2731899572","https://openalex.org/W4230611425","https://openalex.org/W4405112912","https://openalex.org/W4294635752","https://openalex.org/W2375584271","https://openalex.org/W4304166257"],"abstract_inverted_index":{"Segmenting":[0],"a":[1,46,67,80,121,125,147,188,195,203],"breast":[2,228,247],"ultrasound":[3],"image":[4,23,101,169],"is":[5,102,194,223],"still":[6],"challenging":[7],"due":[8],"to":[9,54,245],"the":[10,17,20,28,100,116,136,144],"presence":[11],"of":[12,22,123,127,133,149,153,191],"speckle":[13],"noise,":[14],"dependency":[15],"on":[16,75,98,107,170,179],"operator,":[18],"and":[19,84,94,129,151],"variation":[21],"quality.":[24],"This":[25,40,193,217],"paper":[26],"presents":[27],"UltraSegNet":[29,175,237],"architecture":[30],"that":[31,59,73,91,201,236],"addresses":[32],"these":[33,156],"challenges":[34],"through":[35],"three":[36,43],"key":[37],"technical":[38],"innovations:":[39],"work":[41,234],"adds":[42],"things:":[44],"(1)":[45],"changed":[47],"ResNet-50":[48],"backbone":[49],"with":[50,146,206],"sequential":[51],"3":[52],"convolutions":[53],"keep":[55],"fine":[56],"anatomical":[57],"details":[58],"are":[60,158],"needed":[61],"for":[62,226],"finding":[63],"lesion":[64,221],"boundaries;":[65],"(2)":[66],"computationally":[68],"efficient":[69],"regional":[70],"attention":[71],"mechanism":[72],"works":[74],"high-resolution":[76],"features":[77,96],"without":[78],"using":[79],"transformer\u2019s":[81],"extra":[82],"memory;":[83],"(3)":[85],"an":[86,130],"adaptive":[87],"feature":[88],"fusion":[89],"strategy":[90],"changes":[92],"local":[93],"global":[95],"based":[97],"how":[99],"being":[103],"used.":[104],"Extensive":[105],"evaluation":[106],"two":[108],"distinct":[109],"datasets":[110],"demonstrates":[111],"UltraSegNet\u2019s":[112],"superior":[113],"performance:":[114],"On":[115],"BUSI":[117],"dataset,":[118,138],"it":[119,139],"obtains":[120],"precision":[122,148],"0.915,":[124],"recall":[126,152],"0.908,":[128],"F1":[131],"score":[132],"0.911.":[134],"In":[135],"UDAIT":[137],"achieves":[140],"robust":[141],"performance":[142],"across":[143],"board,":[145],"0.901":[150],"0.894.":[154],"Importantly,":[155],"improvements":[157],"achieved":[159],"at":[160],"clinically":[161],"feasible":[162],"computation":[163],"times,":[164],"taking":[165],"235":[166],"ms":[167],"per":[168],"standard":[171,210],"GPU":[172],"hardware.":[173],"Notably,":[174],"does":[176],"amazingly":[177],"well":[178],"difficult":[180],"small":[181,220],"lesions":[182],"(less":[183],"than":[184],"10":[185],"mm),":[186],"achieving":[187],"detection":[189,222],"accuracy":[190],"0.891.":[192],"huge":[196],"improvement":[197,218],"over":[198],"traditional":[199],"methods":[200],"have":[202],"hard":[204],"time":[205],"small-scale":[207],"features,":[208],"as":[209],"models":[211],"can":[212,238],"only":[213],"achieve":[214],"0.63\u20130.71":[215],"accuracy.":[216,250],"in":[219,242],"particularly":[224],"crucial":[225],"early-stage":[227],"cancer":[229,248],"identification.":[230],"Results":[231],"from":[232],"this":[233],"demonstrate":[235],"be":[239],"practically":[240],"deployable":[241],"clinical":[243],"workflows":[244],"improve":[246],"screening":[249]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}