{"id":"https://openalex.org/W4415974515","doi":"https://doi.org/10.1016/j.procs.2025.09.579","title":"Hybrid Transformer-Based Framework for Multi-Type Lung Cancer Detection Using Attention-Guided Feature Fusion","display_name":"Hybrid Transformer-Based Framework for Multi-Type Lung Cancer Detection Using Attention-Guided Feature Fusion","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415974515","doi":"https://doi.org/10.1016/j.procs.2025.09.579"},"language":"en","primary_location":{"id":"doi:10.1016/j.procs.2025.09.579","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.579","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1016/j.procs.2025.09.579","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5107994490","display_name":"T Revathi","orcid":null},"institutions":[{"id":"https://openalex.org/I144614875","display_name":"Alliance University","ror":"https://ror.org/03f4gsr42","country_code":"IN","type":"education","lineage":["https://openalex.org/I144614875"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Revathi T","raw_affiliation_strings":["Assistant Professor, ASAC, Alliance University, Bangalore and 562106, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Assistant Professor, ASAC, Alliance University, Bangalore and 562106, India","institution_ids":["https://openalex.org/I144614875"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111353282","display_name":"Ezil Sam Leni A","orcid":null},"institutions":[{"id":"https://openalex.org/I144614875","display_name":"Alliance University","ror":"https://ror.org/03f4gsr42","country_code":"IN","type":"education","lineage":["https://openalex.org/I144614875"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ezil Sam Leni A","raw_affiliation_strings":["Professor, ASAC, Alliance University, Bangalore and 562106, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Professor, ASAC, Alliance University, Bangalore and 562106, India","institution_ids":["https://openalex.org/I144614875"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5107994490"],"corresponding_institution_ids":["https://openalex.org/I144614875"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"270","issue":null,"first_page":"4533","last_page":"4542"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.4296000003814697,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.4296000003814697,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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":0.1454000025987625,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.12839999794960022,"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/classifier","display_name":"Classifier (UML)","score":0.6026999950408936},{"id":"https://openalex.org/keywords/lung-cancer","display_name":"Lung cancer","score":0.5472999811172485},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49869999289512634},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.48410001397132874},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.46959999203681946},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.450300008058548}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8476999998092651},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6255000233650208},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6026999950408936},{"id":"https://openalex.org/C2776256026","wikidata":"https://www.wikidata.org/wiki/Q47912","display_name":"Lung cancer","level":2,"score":0.5472999811172485},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49869999289512634},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.48410001397132874},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.46959999203681946},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.450300008058548},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3953000009059906},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3634999990463257},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3337000012397766},{"id":"https://openalex.org/C3019816032","wikidata":"https://www.wikidata.org/wiki/Q2575340","display_name":"Cancer treatment","level":3,"score":0.31119999289512634},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2606000006198883}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.procs.2025.09.579","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.579","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.procs.2025.09.579","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.579","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W3138516171","https://openalex.org/W4396721003","https://openalex.org/W4401659522","https://openalex.org/W4402013271","https://openalex.org/W4403636027","https://openalex.org/W4404563849","https://openalex.org/W4405488452","https://openalex.org/W4405880220","https://openalex.org/W4406190226","https://openalex.org/W4406730677","https://openalex.org/W4406947451","https://openalex.org/W4407128430","https://openalex.org/W4408443006","https://openalex.org/W4408859110"],"related_works":[],"abstract_inverted_index":{"Lung":[0,154],"cancer":[1,33,222,291],"is":[2],"among":[3],"the":[4,46,49,61,77,85,96,109,120,150,166,175,180,228,231,251,263,267],"deadliest":[5],"cancers":[6],"worldwide,":[7],"and":[8,48,56,139,182,189,200,209,217,236,248,258,270,304],"early,":[9],"accurate":[10,218],"diagnosis":[11],"can":[12],"vastly":[13],"improve":[14],"survival.":[15],"To":[16],"address":[17],"this":[18],"work,":[19],"we":[20],"present":[21],"a":[22,27,67,144,215,286,315],"bool":[23],"solution":[24],"based":[25],"on":[26,35,149],"transformer-based":[28],"integrated":[29],"framework":[30,178],"for":[31,124,289],"lung":[32,128,221,290],"detection":[34],"multi-type":[36],"chest":[37,161],"X-rays.":[38],"The":[39,63,116],"model":[40,265],"combines":[41],"two":[42],"state-of-the-art":[43,268],"Transformer-based":[44],"architectures,":[45],"Swin-Transformer":[47],"Vision":[50],"Transformer":[51,65],"(ViT),":[52],"to":[53,71,84,103,164,220,319],"mine":[54],"local":[55,259],"global":[57,201,257],"visual":[58],"features":[59,90],"in":[60,205,255,293,322],"images.":[62],"Swin":[64],"uses":[66],"shifted":[68],"window":[69],"mechanism":[70],"capture":[72],"hierarchical":[73,199],"spatial":[74],"features,":[75,107],"while":[76,113],"ViT":[78],"helps":[79],"bring":[80],"more":[81],"contextual":[82],"understanding":[83],"whole":[86],"image.":[87],"These":[88,272],"complementary":[89],"are":[91],"then":[92],"merged":[93],"intelligently":[94],"at":[95],"Attention":[97],"Fusion":[98],"Layer,":[99],"which":[100],"adaptively":[101],"learns":[102],"weigh":[104],"those":[105],"extracted":[106],"enriching":[108],"most":[110],"representative":[111],"parts":[112],"suppressing":[114],"redundancy.":[115],"MLP":[117,213],"classifier":[118],"receives":[119],"cleaned":[121],"feature":[122,202],"vector":[123],"multi-class":[125],"classification":[126,244,292],"of":[127,146,158,168,230,253],"conditions":[129],"such":[130],"as":[131,314],"adenocarcinoma,":[132],"large":[133],"cell":[134,137],"carcinoma,":[135,138],"squamous":[136],"normal.":[140],"We":[141],"have":[142],"performed":[143],"series":[145],"wide-ranging":[147],"experiments":[148],"publicly":[151],"accessible":[152],"Figshare":[153],"Cancer":[155],"dataset,":[156],"consisting":[157],"expert-labelled,":[159],"variegated":[160],"X-ray":[162],"images,":[163],"demonstrate":[165],"potential":[167,313],"our":[169,195],"method.":[170],"Experimental":[171],"results":[172,239,300],"indicate":[173],"that":[174,198,275],"proposed":[176,232],"hybrid":[177,276],"outperforms":[179,266],"UperNet":[181,235,247,269],"Swin-Unet":[183],"models,":[184],"yielding":[185],"superior":[186,243],"accuracy,":[187,284],"precision,":[188],"F1-score":[190],"across":[191],"all":[192],"classes.":[193],"Overall,":[194],"findings":[196,273],"suggest":[197,274],"extractors":[203],"fused":[204],"an":[206,212],"attention-based":[207,280],"way":[208],"classified":[210],"using":[211],"offer":[214],"strong":[216],"approach":[219,288,310],"detection.":[223],"An":[224],"ablation":[225],"study":[226],"evaluates":[227],"contributions":[229],"transformer":[233,277],"architecture,":[234],"Swin-Unet.":[237,271],"Simulated":[238],"show":[240],"ViT-Swin-AGFF":[241,264],"achieves":[242],"performance":[245],"over":[246],"Swin-Unet,":[249],"highlighting":[250],"efficacy":[252],"AGFF":[254],"combining":[256],"features.":[260],"For":[261],"segmentation,":[262],"architectures":[278],"with":[279,301],"fusion":[281],"enhance":[282],"diagnostic":[283,317],"offering":[285],"promising":[287],"clinical":[294,324],"settings.":[295],"Future":[296],"work":[297],"will":[298],"validate":[299],"real-world":[302],"datasets":[303],"explore":[305],"segmentation":[306],"mask":[307],"integration.":[308],"This":[309],"has":[311],"great":[312],"computer-aided":[316],"tool":[318],"assist":[320],"radiologists":[321],"making":[323],"decisions.":[325]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-06T00:00:00"}