{"id":"https://openalex.org/W4415593795","doi":"https://doi.org/10.1109/access.2025.3626224","title":"Multiclass Intracranial Hemorrhage Detection and Confidence Aware Triage via Deep Ensemble Learning","display_name":"Multiclass Intracranial Hemorrhage Detection and Confidence Aware Triage via Deep Ensemble Learning","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415593795","doi":"https://doi.org/10.1109/access.2025.3626224"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3626224","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3626224","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.2025.3626224","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104939200","display_name":"Abhay Chaudhary","orcid":"https://orcid.org/0009-0004-7337-9258"},"institutions":[{"id":"https://openalex.org/I3129773123","display_name":"Bennett University","ror":"https://ror.org/00an5hx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I3129773123"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Abhay Chaudhary","raw_affiliation_strings":["Bennett University, Greater Noida, Uttar Pradesh, India","Bennett University, Greater Noida, India"],"raw_orcid":"https://orcid.org/0009-0004-7337-9258","affiliations":[{"raw_affiliation_string":"Bennett University, Greater Noida, Uttar Pradesh, India","institution_ids":["https://openalex.org/I3129773123"]},{"raw_affiliation_string":"Bennett University, Greater Noida, India","institution_ids":["https://openalex.org/I3129773123"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104939199","display_name":"Yana Gaur","orcid":"https://orcid.org/0009-0000-7629-7750"},"institutions":[{"id":"https://openalex.org/I3129773123","display_name":"Bennett University","ror":"https://ror.org/00an5hx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I3129773123"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Yana Gaur","raw_affiliation_strings":["Bennett University, Greater Noida, Uttar Pradesh, India","Bennett University, Greater Noida, India"],"raw_orcid":"https://orcid.org/0009-0000-7629-7750","affiliations":[{"raw_affiliation_string":"Bennett University, Greater Noida, Uttar Pradesh, India","institution_ids":["https://openalex.org/I3129773123"]},{"raw_affiliation_string":"Bennett University, Greater Noida, India","institution_ids":["https://openalex.org/I3129773123"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087542455","display_name":"Ajith Abraham","orcid":"https://orcid.org/0000-0002-0169-6738"},"institutions":[{"id":"https://openalex.org/I4210116741","display_name":"Innopolis University","ror":"https://ror.org/02b7jh107","country_code":"RU","type":"education","lineage":["https://openalex.org/I4210116741"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Ajith Abraham","raw_affiliation_strings":["Research Center, Artificial Intelligence Institute, Innopolis University, Innopolis, Russia","SAI University, Chennai, India"],"raw_orcid":"https://orcid.org/0000-0002-0169-6738","affiliations":[{"raw_affiliation_string":"Research Center, Artificial Intelligence Institute, Innopolis University, Innopolis, Russia","institution_ids":["https://openalex.org/I4210116741"]},{"raw_affiliation_string":"SAI University, Chennai, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019392696","display_name":"Harmandeep Singh","orcid":"https://orcid.org/0000-0002-2619-7871"},"institutions":[{"id":"https://openalex.org/I172982924","display_name":"University College of Medical Sciences","ror":"https://ror.org/01h3fm945","country_code":"IN","type":"education","lineage":["https://openalex.org/I110166357","https://openalex.org/I172982924"]},{"id":"https://openalex.org/I2802539864","display_name":"Guru Teg Bahadur Hospital","ror":"https://ror.org/02v8rz176","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I2802539864"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Harmandeep Singh","raw_affiliation_strings":["Guru Teg Bahadur Hospital, University College of Medical Sciences, Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guru Teg Bahadur Hospital, University College of Medical Sciences, Delhi, India","institution_ids":["https://openalex.org/I172982924","https://openalex.org/I2802539864"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39875515,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"191745","last_page":"191761"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11763","display_name":"Intracerebral and Subarachnoid Hemorrhage Research","score":0.9883999824523926,"subfield":{"id":"https://openalex.org/subfields/2728","display_name":"Neurology"},"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/T11763","display_name":"Intracerebral and Subarachnoid Hemorrhage Research","score":0.9883999824523926,"subfield":{"id":"https://openalex.org/subfields/2728","display_name":"Neurology"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9864000082015991,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/triage","display_name":"Triage","score":0.607699990272522},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5056999921798706},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.4771000146865845},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.4359000027179718},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.42559999227523804},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.4189999997615814},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.41449999809265137},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.3799999952316284}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7789999842643738},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.633400022983551},{"id":"https://openalex.org/C2777120189","wikidata":"https://www.wikidata.org/wiki/Q780067","display_name":"Triage","level":2,"score":0.607699990272522},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5056999921798706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4936000108718872},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.4771000146865845},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.4359000027179718},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.42559999227523804},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.4189999997615814},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.41449999809265137},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.3799999952316284},{"id":"https://openalex.org/C145804949","wikidata":"https://www.wikidata.org/wiki/Q478123","display_name":"Situation awareness","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C2778604727","wikidata":"https://www.wikidata.org/wiki/Q7920328","display_name":"Ventriculostomy","level":3,"score":0.2921999990940094},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C2778863898","wikidata":"https://www.wikidata.org/wiki/Q5422058","display_name":"External ventricular drain","level":3,"score":0.26600000262260437},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.2635999917984009},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.2596000134944916}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3626224","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3626224","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:6faf9ee24d6f4ee6b3e58f6a7a9742d4","is_oa":true,"landing_page_url":"https://doaj.org/article/6faf9ee24d6f4ee6b3e58f6a7a9742d4","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 191745-191761 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3626224","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3626224","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2295598076","https://openalex.org/W2592929672","https://openalex.org/W2767813783","https://openalex.org/W2795774310","https://openalex.org/W2883545264","https://openalex.org/W2896817483","https://openalex.org/W2943644689","https://openalex.org/W2951965145","https://openalex.org/W2961934740","https://openalex.org/W2981941315","https://openalex.org/W3023284086","https://openalex.org/W3138516171","https://openalex.org/W4224289711","https://openalex.org/W4312443924","https://openalex.org/W4319598202","https://openalex.org/W4322501497","https://openalex.org/W4362731331","https://openalex.org/W4379780418","https://openalex.org/W4386849590","https://openalex.org/W4390408938","https://openalex.org/W4399781866","https://openalex.org/W4404578669","https://openalex.org/W4406599698","https://openalex.org/W4408998111"],"related_works":[],"abstract_inverted_index":{"Intracranial":[0,255],"Hemorrhage":[1,256],"is":[2,29],"a":[3,40,105,141,163,194,205,217,249],"critical":[4],"medical":[5],"condition":[6],"characterized":[7],"by":[8],"bleeding":[9],"within":[10],"the":[11,99,124,134,259],"skull,":[12],"frequently":[13],"resulting":[14],"from":[15,53],"traumatic":[16],"brain":[17],"injury,":[18],"aneurysms,":[19],"or":[20],"other":[21],"vascular":[22],"abnormalities.":[23],"A":[24],"rapid":[25],"and":[26,63,66,74,91,97,114,122,162,189,235],"accurate":[27],"diagnosis":[28],"essential,":[30],"as":[31,71],"delays":[32],"can":[33],"have":[34],"life-threatening":[35],"consequences.":[36],"This":[37,182],"paper":[38],"presents":[39],"comprehensive":[41],"automated":[42],"deep":[43],"learning":[44,76],"framework":[45,203],"that":[46,108],"achieves":[47],"robust":[48],"multiclass":[49],"classification":[50],"of":[51,136,175],"ICH":[52,199],"Computed":[54],"Tomography":[55],"scan":[56],"images":[57],"encompassing":[58],"intraparenchymal,":[59],"intraventricular,":[60],"subarachnoid,":[61],"subdural,":[62],"epidural":[64,126],"hemorrhages,":[65],"integrates":[67],"advanced":[68],"techniques":[69],"such":[70],"uncertainty":[72,154,210,237],"estimation":[73,211],"ensemble":[75],"to":[77,120,150,180],"support":[78],"clinical":[79,187],"decision-making.":[80],"Five":[81],"state-of-the-art":[82],"vision":[83],"backbones":[84],"-":[85,93],"ConvNeXt,":[86],"CoaTNet,":[87],"Swin":[88],"Transformer,":[89],"ViT-B,":[90],"DeiT-S":[92],"were":[94],"systematically":[95],"benchmarked,":[96],"leveraging":[98],"three":[100],"strongest":[101],"networks":[102],"we":[103],"constructed":[104],"gradient-boosted":[106],"meta-ensemble":[107,233],"achieved":[109],"0.948":[110],"accuracy,":[111],"0.970":[112],"micro-F1,":[113],"0.967":[115],"macro-F1,":[116],"raising":[117],"macro":[118],"recall":[119],"0.957":[121],"recovering":[123],"rare":[125],"subtype":[127],"(F1":[128],"=":[129,160],"0.83).":[130],"Beyond":[131],"accuracy":[132],"gains,":[133],"novelty":[135],"this":[137,239],"work":[138],"lies":[139],"in":[140,165,197,212,258],"confidence-aware":[142],"triage":[143,257],"design:":[144],"dropout":[145],"was":[146],"retained":[147],"at":[148],"inference":[149],"generate":[151],"Monte":[152],"Carlo":[153],"estimates,":[155],"enabling":[156],"calibrated":[157],"probabilities":[158],"(ECE":[159],"2.8%)":[161],"workflow":[164],"which":[166],"high-confidence":[167],"cases":[168],"are":[169,178],"automatically":[170],"cleared":[171],"while":[172],"only":[173,243],"\u22488%":[174],"uncertain":[176],"slices":[177],"routed":[179],"radiologists.":[181],"selective":[183],"strategy":[184],"both":[185],"reduces":[186],"workload":[188],"transparently":[190],"communicates":[191],"algorithmic":[192],"doubt,":[193],"capability":[195],"absent":[196],"prior":[198],"classifiers.":[200],"The":[201],"complete":[202],"processes":[204],"head":[206],"CT":[207],"volume":[208],"with":[209,224],"under":[213],"four":[214],"minutes":[215],"on":[216],"single":[218],"GPU,":[219],"demonstrating":[220],"deployment-ready":[221],"throughput":[222],"compatible":[223],"emergency":[225],"radiology":[226],"timelines.":[227],"By":[228],"combining":[229],"systematic":[230],"backbone":[231],"benchmarking,":[232],"fusion,":[234],"slice-level":[236],"quantification,":[238],"study":[240],"provides":[241],"not":[242],"improved":[244],"rare-subtype":[245],"sensitivity":[246],"but":[247],"also":[248],"practical":[250],"foundation":[251],"for":[252],"real-time,":[253],"trustworthy":[254],"acute-care":[260],"setting.":[261]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-28T00:00:00"}
