{"id":"https://openalex.org/W4414603188","doi":"https://doi.org/10.1109/access.2025.3615654","title":"HFT-Net: Hybrid Fusion Transformer Network for Multi-Source Breast Cancer Classification","display_name":"HFT-Net: Hybrid Fusion Transformer Network for Multi-Source Breast Cancer Classification","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4414603188","doi":"https://doi.org/10.1109/access.2025.3615654"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3615654","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3615654","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3615654","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034192245","display_name":"Kadir Guzel","orcid":"https://orcid.org/0000-0002-3664-6810"},"institutions":[{"id":"https://openalex.org/I4101805","display_name":"Y\u0131ld\u0131z Technical University","ror":"https://ror.org/0547yzj13","country_code":"TR","type":"education","lineage":["https://openalex.org/I4101805"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Kadir Guzel","raw_affiliation_strings":["Department of Computer Engineering, Yildiz Technical University (YTU), Davutpasa Campus, Istanbul, T&#x00FC;rkiye","Dpt. of Computer Engineering, Yildiz Technical University (YTU), Davutpasa Campus, Istanbul, Turkey"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Yildiz Technical University (YTU), Davutpasa Campus, Istanbul, T&#x00FC;rkiye","institution_ids":["https://openalex.org/I4101805"]},{"raw_affiliation_string":"Dpt. of Computer Engineering, Yildiz Technical University (YTU), Davutpasa Campus, Istanbul, Turkey","institution_ids":["https://openalex.org/I4101805"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082045666","display_name":"G\u00f6khan Bilgin","orcid":"https://orcid.org/0000-0002-5532-477X"},"institutions":[{"id":"https://openalex.org/I4101805","display_name":"Y\u0131ld\u0131z Technical University","ror":"https://ror.org/0547yzj13","country_code":"TR","type":"education","lineage":["https://openalex.org/I4101805"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Gokhan Bilgin","raw_affiliation_strings":["Department of Computer Engineering, Yildiz Technical University (YTU), Davutpasa Campus, Istanbul, T&#x00FC;rkiye","Dpt. of Computer Engineering, Yildiz Technical University (YTU), Davutpasa Campus, Istanbul, Turkey"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Yildiz Technical University (YTU), Davutpasa Campus, Istanbul, T&#x00FC;rkiye","institution_ids":["https://openalex.org/I4101805"]},{"raw_affiliation_string":"Dpt. of Computer Engineering, Yildiz Technical University (YTU), Davutpasa Campus, Istanbul, Turkey","institution_ids":["https://openalex.org/I4101805"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5034192245"],"corresponding_institution_ids":["https://openalex.org/I4101805"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.4849,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91632382,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"13","issue":null,"first_page":"170126","last_page":"170146"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9943000078201294,"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":0.9943000078201294,"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/T10885","display_name":"Gene expression and cancer classification","score":0.9269999861717224,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9194999933242798,"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/discriminative-model","display_name":"Discriminative model","score":0.7717999815940857},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5972999930381775},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5662999749183655},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5378000140190125},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4772999882698059},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4726000130176544},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46959999203681946},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4156999886035919}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8194000124931335},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7717999815940857},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7224000096321106},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5972999930381775},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5662999749183655},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5453000068664551},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5378000140190125},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4772999882698059},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4726000130176544},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46959999203681946},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4156999886035919},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4077000021934509},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.38600000739097595},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3377000093460083},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33489999175071716},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.33239999413490295},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.296099990606308},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2671999931335449}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3615654","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3615654","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:900226454eee4304a4957d5c04755581","is_oa":true,"landing_page_url":"https://doaj.org/article/900226454eee4304a4957d5c04755581","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 170126-170146 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3615654","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3615654","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W1903029394","https://openalex.org/W2183341477","https://openalex.org/W2343160907","https://openalex.org/W2344480160","https://openalex.org/W2531409750","https://openalex.org/W2744692634","https://openalex.org/W2885824038","https://openalex.org/W2899332989","https://openalex.org/W2929968583","https://openalex.org/W2949306187","https://openalex.org/W2963446712","https://openalex.org/W2963460810","https://openalex.org/W2966647688","https://openalex.org/W2989695963","https://openalex.org/W3045004532","https://openalex.org/W3091883770","https://openalex.org/W3092028788","https://openalex.org/W3120761542","https://openalex.org/W3138516171","https://openalex.org/W3145185940","https://openalex.org/W3184620780","https://openalex.org/W3200923647","https://openalex.org/W3209367260","https://openalex.org/W3214733388","https://openalex.org/W4225009159","https://openalex.org/W4281386887","https://openalex.org/W4281999467","https://openalex.org/W4283746543","https://openalex.org/W4288391575","https://openalex.org/W4308333450","https://openalex.org/W4313577028","https://openalex.org/W4319299841","https://openalex.org/W4320717537","https://openalex.org/W4385245566","https://openalex.org/W4385661331","https://openalex.org/W4386212637","https://openalex.org/W4387885991","https://openalex.org/W4391235341","https://openalex.org/W4392452416","https://openalex.org/W4393356706","https://openalex.org/W4396611616","https://openalex.org/W4396680671","https://openalex.org/W4398194081","https://openalex.org/W4399557997","https://openalex.org/W4400201371","https://openalex.org/W4404455385","https://openalex.org/W4404688382","https://openalex.org/W4405303938","https://openalex.org/W4405558983","https://openalex.org/W4406260040","https://openalex.org/W4406842983","https://openalex.org/W4406946825","https://openalex.org/W4407046757","https://openalex.org/W4407157026","https://openalex.org/W4407849425","https://openalex.org/W4407956749"],"related_works":[],"abstract_inverted_index":{"Automatic":[0],"classification":[1],"of":[2,23,39,79,114,118,141],"breast":[3],"cancer":[4],"histopathological":[5],"images":[6],"presents":[7],"significant":[8,17],"challenges":[9],"due":[10],"to":[11,61,111],"morphological":[12],"ambiguities":[13],"between":[14],"disease":[15],"subtypes,":[16],"tissue":[18],"heterogeneity,":[19],"and":[20,65,84,93,129,154,172,179,182],"limited":[21],"availability":[22],"high-quality":[24],"labeled":[25],"datasets.":[26,137],"We":[27],"propose":[28],"HFT-Net,":[29,104],"a":[30,56,109,122,139],"hybrid":[31],"deep":[32,80],"learning":[33,83,96],"model":[34,54,147],"that":[35],"addresses":[36],"the":[37,75,112,145,158,165,176,186],"limitations":[38],"traditional":[40],"single-architecture":[41],"approaches":[42],"in":[43,134],"generalizing":[44],"complex":[45],"visual":[46],"patterns.":[47],"Unlike":[48],"conventional":[49],"feature":[50,68],"fusion":[51],"methods,":[52],"our":[53],"employs":[55],"multi-head":[57],"attention":[58],"mechanism":[59],"(MHA)":[60],"enrich":[62],"information":[63],"interaction":[64],"learn":[66],"meaningful":[67],"relationships,":[69],"creating":[70],"more":[71],"discriminative":[72],"representations.":[73],"Despite":[74],"large":[76],"data":[77],"requirement":[78],"models,":[81],"transfer":[82],"fine-tuning":[85],"techniques":[86],"enabled":[87],"high":[88],"success":[89],"with":[90,151],"few":[91],"samples,":[92],"an":[94],"efficient":[95],"process":[97],"was":[98,106],"achieved":[99],"by":[100],"adapting":[101],"pre-trained":[102],"models.":[103],"which":[105],"developed":[107],"as":[108],"solution":[110],"problem":[113],"low":[115],"generalization":[116],"capacity":[117],"models":[119],"optimized":[120],"for":[121,126],"single":[123],"dataset,":[124,161,169],"aims":[125],"balanced":[127],"performance":[128,150],"consistent":[130],"results":[131],"were":[132],"obtained":[133],"three":[135],"different":[136],"As":[138],"result":[140],"extensive":[142],"experimental":[143],"evaluations,":[144],"proposed":[146],"shows":[148],"competitive":[149],"95.08%":[152],"accuracy":[153,163,171,181],"0.95":[155],"F1-score":[156,174,184],"on":[157,164,175,185],"8-class":[159],"BreakHis":[160],"92.00%":[162],"BACH":[166,177],"secret":[167],"test":[168],"92.50%":[170],"0.92":[173],"dataset":[178],"58.07%":[180],"0.58":[183],"BRACS":[187],"dataset.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
