{"id":"https://openalex.org/W7125922250","doi":"https://doi.org/10.1109/access.2026.3658607","title":"A Bayesian Backed DeepLabV3+ Customized Hybrid-Depth Framework for Brain Tumor Segmentation and Classification","display_name":"A Bayesian Backed DeepLabV3+ Customized Hybrid-Depth Framework for Brain Tumor Segmentation and Classification","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7125922250","doi":"https://doi.org/10.1109/access.2026.3658607"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3658607","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3658607","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.2026.3658607","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079350994","display_name":"Muhammad Sami Ullah","orcid":"https://orcid.org/0000-0002-4303-672X"},"institutions":[{"id":"https://openalex.org/I99165933","display_name":"University of Gujrat","ror":"https://ror.org/01xe5fb92","country_code":"PK","type":"education","lineage":["https://openalex.org/I99165933"]}],"countries":["PK"],"is_corresponding":true,"raw_author_name":"Muhammad Sami Ullah","raw_affiliation_strings":["Department of Computer Science, University of Gujrat, Gujrat, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Gujrat, Gujrat, Pakistan","institution_ids":["https://openalex.org/I99165933"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Muhammad Attique Khan","orcid":"https://orcid.org/0000-0001-5723-3858"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Muhammad Attique Khan","raw_affiliation_strings":["University College, Korea University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-5723-3858","affiliations":[{"raw_affiliation_string":"University College, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123576888","display_name":"Saeed Iqbal","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Saeed Iqbal","raw_affiliation_strings":["College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-3176-4658","affiliations":[{"raw_affiliation_string":"College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124068978","display_name":"Shrooq Alsenan","orcid":null},"institutions":[{"id":"https://openalex.org/I106778892","display_name":"Princess Nourah bint Abdulrahman University","ror":"https://ror.org/05b0cyh02","country_code":"SA","type":"education","lineage":["https://openalex.org/I106778892"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Shrooq Alsenan","raw_affiliation_strings":["Information Systems Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Systems Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia","institution_ids":["https://openalex.org/I106778892"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004989050","display_name":"Areej Alasiry","orcid":"https://orcid.org/0000-0003-4088-5942"},"institutions":[{"id":"https://openalex.org/I82952536","display_name":"King Khalid University","ror":"https://ror.org/052kwzs30","country_code":"SA","type":"education","lineage":["https://openalex.org/I82952536"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Areej Alasiry","raw_affiliation_strings":["College of Computer Science, King Khalid University, Abha, Saudi Arabia"],"raw_orcid":"https://orcid.org/0000-0003-4088-5942","affiliations":[{"raw_affiliation_string":"College of Computer Science, King Khalid University, Abha, Saudi Arabia","institution_ids":["https://openalex.org/I82952536"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014832215","display_name":"Mehrez Marzougui","orcid":"https://orcid.org/0000-0003-0445-5371"},"institutions":[{"id":"https://openalex.org/I82952536","display_name":"King Khalid University","ror":"https://ror.org/052kwzs30","country_code":"SA","type":"education","lineage":["https://openalex.org/I82952536"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Mehrez Marzougui","raw_affiliation_strings":["College of Computer Science, King Khalid University, Abha, Saudi Arabia"],"raw_orcid":"https://orcid.org/0000-0003-0445-5371","affiliations":[{"raw_affiliation_string":"College of Computer Science, King Khalid University, Abha, Saudi Arabia","institution_ids":["https://openalex.org/I82952536"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103058644","display_name":"Yunyoung Nam","orcid":"https://orcid.org/0000-0002-3318-9394"},"institutions":[{"id":"https://openalex.org/I24541011","display_name":"Soonchunhyang University","ror":"https://ror.org/03qjsrb10","country_code":"KR","type":"education","lineage":["https://openalex.org/I24541011"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yunyoung Nam","raw_affiliation_strings":["Emotional and Intelligent Child Care Convergence Research Center, Soonchunhyang University, Asan-si, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-3318-9394","affiliations":[{"raw_affiliation_string":"Emotional and Intelligent Child Care Convergence Research Center, Soonchunhyang University, Asan-si, Republic of Korea","institution_ids":["https://openalex.org/I24541011"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5079350994"],"corresponding_institution_ids":["https://openalex.org/I99165933"],"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":0.1442983,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"20328","last_page":"20354"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.5565999746322632,"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"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.5565999746322632,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.2996000051498413,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.062300000339746475,"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/context","display_name":"Context (archaeology)","score":0.59170001745224},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5723000168800354},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5684000253677368},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5313000082969666},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49959999322891235},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4830000102519989},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4609000086784363},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4546000063419342}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8084999918937683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6629999876022339},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.59170001745224},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5723000168800354},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5684000253677368},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5313000082969666},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49959999322891235},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4830000102519989},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4609000086784363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45680001378059387},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4546000063419342},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4341999888420105},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40549999475479126},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37560001015663147},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.37380000948905945},{"id":"https://openalex.org/C2779130545","wikidata":"https://www.wikidata.org/wiki/Q233309","display_name":"Brain tumor","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.31209999322891235},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.3095000088214874},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2854999899864197},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.27390000224113464}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3658607","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3658607","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"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3658607","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3658607","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":[{"id":"https://openalex.org/F4320321301","display_name":"Soonchunhyang University","ror":"https://ror.org/03qjsrb10"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W116482931","https://openalex.org/W1849277567","https://openalex.org/W2004915807","https://openalex.org/W2015288103","https://openalex.org/W2038362839","https://openalex.org/W2108598243","https://openalex.org/W2113714330","https://openalex.org/W2147567199","https://openalex.org/W2163922914","https://openalex.org/W2194775991","https://openalex.org/W2366536035","https://openalex.org/W2618530766","https://openalex.org/W2767106145","https://openalex.org/W2787091153","https://openalex.org/W2899283552","https://openalex.org/W2904208441","https://openalex.org/W2945839551","https://openalex.org/W2963446712","https://openalex.org/W2986661129","https://openalex.org/W3118929067","https://openalex.org/W3127751679","https://openalex.org/W3128646645","https://openalex.org/W3176178968","https://openalex.org/W3198503790","https://openalex.org/W4205890830","https://openalex.org/W4212875960","https://openalex.org/W4224254448","https://openalex.org/W4226017353","https://openalex.org/W4238407521","https://openalex.org/W4250075238","https://openalex.org/W4282548202","https://openalex.org/W4282964635","https://openalex.org/W4289794192","https://openalex.org/W4311415873","https://openalex.org/W4321851617","https://openalex.org/W4362500858","https://openalex.org/W4363650848","https://openalex.org/W4381308590","https://openalex.org/W4385245566","https://openalex.org/W4386457665","https://openalex.org/W4387366493","https://openalex.org/W4387640747","https://openalex.org/W4390884647","https://openalex.org/W4396677248","https://openalex.org/W4396829224","https://openalex.org/W4399449895","https://openalex.org/W4400593383","https://openalex.org/W4402220158","https://openalex.org/W4403619542","https://openalex.org/W4405823766","https://openalex.org/W4405823803"],"related_works":[],"abstract_inverted_index":{"Efficient":[0],"diagnosis,":[1],"treatment":[2],"planning,":[3],"and":[4,18,44,59,77,103,124,137,155,165,189,211,218,236,253,255,262,266,281,287,298,306,330,348,365,384],"patient":[5,357],"care":[6],"requires":[7],"that":[8,225],"brain":[9,116,126,246,289,327,398],"tumors":[10],"in":[11,197,310,325,352,376],"MRI":[12,57,248],"images":[13],"must":[14],"be":[15],"accurately":[16],"segmented":[17],"classified.":[19],"Many":[20],"deep":[21],"learning":[22],"practices":[23],"are":[24,65],"used":[25],"to":[26,147,231,244],"perform":[27],"these":[28],"tasks.":[29],"However,":[30],"prominent":[31],"performance":[32,191,317],"gaps":[33,50,64,375],"like":[34],"insufficient":[35],"hierarchical":[36,210,265],"feature":[37,213,238,268,386],"extraction,":[38],"lesser-known":[39],"biological":[40],"delineation":[41],"of":[42,80,92,100,105,115,133,139,152,161,378,381],"tumor":[43,61,127,134,141,153,247,256,290,328,399],"limited":[45],"uncertainty":[46,79],"modeling":[47],"exist.":[48],"These":[49,63],"specifically":[51],"occur":[52],"while":[53],"dealing":[54],"with":[55,193],"multi-modal":[56,131,385],"data":[58],"heterogenous":[60],"shapes.":[62],"addressed":[66],"by":[67,96,157,228,301,372],"introducing":[68],"a":[69,150,201,222,340,360,392],"Bayesian":[70,101],"based":[71],"DeepLabV3+":[72],"architecture.":[73],"It":[74,144],"quantifies":[75],"aleatoric":[76],"epistemic":[78],"segmentation.":[81],"Hence,":[82,388],"it":[83,220],"provides":[84],"improved":[85],"reliability":[86],"on":[87,122,163,167],"clinical":[88],"decisions.":[89],"The":[90,118,240,264,276,293,315,336],"selection":[91,380],"architecture":[93,119,278],"is":[94,110,120,178,206,221,226,273],"backed":[95],"the":[97,168,229,285,303,354,362,370,389],"promising":[98],"ability":[99],"inference":[102],"preservation":[104],"multi-scale":[106],"spatial":[107],"context":[108],"which":[109,129,174,208],"suited":[111],"for":[112,171,270,284,322,396],"complex":[113],"morphology":[114],"tumors.":[117],"tested":[121],"BraTS23":[123,164,286],"Figshare":[125,169,288],"datasets":[128],"has":[130],"annotations":[132],"sub":[135],"regions":[136],"consist":[138],"multiple":[140],"recognitions":[142],"respectively.":[143,292],"performed":[145,242],"remarkably":[146],"properly":[148],"manage":[149],"range":[151],"forms":[154],"sizes":[156],"achieving":[158],"Dice":[159],"scores":[160],"95.86%":[162],"92.80%":[166],"dataset":[170],"segmentation":[172],"tasks,":[173],"its":[175,296,308,320,331],"statistical":[176],"significance":[177],"proven":[179],"through":[180],"several":[181],"cross-validation":[182],"trials":[183],"(<italic":[184],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[185],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">p</i>":[186],"<":[187],"0.05)":[188],"superior":[190],"compared":[192],"leading":[194],"approaches":[195],"reported":[196],"recent":[198],"literature.":[199],"Furthermore,":[200],"unique":[202],"Hybrid-Depth":[203,304,366],"classification":[204,271],"framework":[205,241,338],"developed,":[207],"merges":[209],"coarse-level":[212],"extraction":[214,269],"using":[215],"dual-branch":[216],"processing":[217],"self-attention.":[219],"design":[223],"choice":[224],"justified":[227],"requirement":[230],"explicitly":[232],"characterize":[233],"cross-modal":[234],"interdependence":[235],"mitigate":[237],"redundancy.":[239],"effectively":[243],"classify":[245],"modalities":[249],"(Flair,":[250],"T1,":[251],"T2":[252],"T1ce)":[254],"type":[257],"classes":[258],"(i.e.":[259],"gliomas,":[260],"meningiomas":[261],"pituitary).":[263],"coarser":[267],"task":[272],"done":[274],"gracefully.":[275],"proposed":[277,337,363],"achieved":[279],"98.80%":[280],"95.40%":[282],"accuracy":[283,347],"datasets,":[291],"work":[294],"demonstrates":[295,318],"uniqueness":[297],"practical":[299,323],"usefulness":[300],"defining":[302],"mechanism":[305],"detailing":[307],"role":[309],"increasing":[311,344],"discriminative":[312],"representational":[313],"learning.":[314,387],"framework\u2019s":[316],"both":[319],"potential":[321],"use":[324],"automated":[326,397],"diagnosis":[329],"superiority":[332],"over":[333],"current":[334],"approaches.":[335],"provided":[339],"major":[341],"step":[342],"toward":[343],"diagnostic":[345],"efficiency,":[346],"assisting":[349],"healthcare":[350],"providers":[351],"providing":[353],"best":[355],"possible":[356],"care.":[358],"In":[359],"nutshell,":[361],"Bayesian-DeepLabV3+":[364],"frameworks":[367],"jointly":[368],"enhance":[369],"field":[371],"bridging":[373],"methodological":[374],"quantification":[377],"uncertainty,":[379],"dataset-driven":[382],"architecture,":[383],"approach":[390],"offers":[391],"robust":[393],"AI":[394],"pipeline":[395],"diagnosis.":[400]},"counts_by_year":[],"updated_date":"2026-02-13T13:36:01.753593","created_date":"2026-01-29T00:00:00"}