{"id":"https://openalex.org/W7204454020","doi":"https://doi.org/10.51584/ijrias.2026.11080007","title":"Hybrid U-Net++\u2013Vision Transformer Fusion for Accurate Brain MRI Image Segmentation and classifications","display_name":"Hybrid U-Net++\u2013Vision Transformer Fusion for Accurate Brain MRI Image Segmentation and classifications","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7204454020","doi":"https://doi.org/10.51584/ijrias.2026.11080007"},"language":"en","primary_location":{"id":"doi:10.51584/ijrias.2026.11080007","doi":"https://doi.org/10.51584/ijrias.2026.11080007","is_oa":true,"landing_page_url":"https://doi.org/10.51584/ijrias.2026.11080007","pdf_url":"https://rsisinternational.org/journals/ijrias/uploads/vol11-iss8-pg125-139-202608_pdf.pdf","source":{"id":"https://openalex.org/S4210227786","display_name":"International Journal of Research and Innovation in Applied Science","issn_l":"2454-6194","issn":["2454-6194"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"International Journal of Research and Innovation in Applied Science","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://rsisinternational.org/journals/ijrias/uploads/vol11-iss8-pg125-139-202608_pdf.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115608725","display_name":"Malathi Janapati","orcid":"https://orcid.org/0000-0001-7096-0435","observed_orcids":["https://orcid.org/0000-0001-7096-0435"]},"institutions":[{"id":"https://openalex.org/I177001074","display_name":"Acharya Nagarjuna University","ror":"https://ror.org/02mfapa96","country_code":"IN","type":"education","lineage":["https://openalex.org/I177001074"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Malathi Janapati","raw_affiliation_strings":["Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur","India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur","institution_ids":["https://openalex.org/I177001074"]},{"raw_affiliation_string":"India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081655542","display_name":"Shaheda Akthar","orcid":"https://orcid.org/0009-0000-8479-4060","observed_orcids":["https://orcid.org/0009-0000-8479-4060"]},"institutions":[{"id":"https://openalex.org/I177001074","display_name":"Acharya Nagarjuna University","ror":"https://ror.org/02mfapa96","country_code":"IN","type":"education","lineage":["https://openalex.org/I177001074"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shaheda Akthar","raw_affiliation_strings":["Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, India, and Department of Computer Science, Government College for Women (A), Guntur","India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, India, and Department of Computer Science, Government College for Women (A), Guntur","institution_ids":["https://openalex.org/I177001074"]},{"raw_affiliation_string":"India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I177001074"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":"8","first_page":"125","last_page":"139"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9883000254631042,"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"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9883000254631042,"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":0.01119999960064888,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0,"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/brain-mri","display_name":"brain MRI","score":0.7210000157356262},{"id":"https://openalex.org/keywords/brain-tumor-segmentation","display_name":"brain tumor segmentation","score":0.9869999885559082},{"id":"https://openalex.org/keywords/glioma","display_name":"glioma","score":0.652999997138977},{"id":"https://openalex.org/keywords/meningioma","display_name":"meningioma","score":1.0},{"id":"https://openalex.org/keywords/pituitary-tumors","display_name":"pituitary tumors","score":0.9980000257492065},{"id":"https://openalex.org/keywords/multi-stage-feature-fusion","display_name":"multi-stage feature fusion","score":0.8429999947547913},{"id":"https://openalex.org/keywords/tumor-classification","display_name":"tumor classification","score":0.6769999861717224},{"id":"https://openalex.org/keywords/region-of-interest-classification","display_name":"region of interest classification","score":0.6520000100135803},{"id":"https://openalex.org/keywords/computer-aided-diagnosis","display_name":"computer-aided diagnosis","score":0.8410000205039978}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7731000185012817},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7052000164985657},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6966999769210815},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5479000210762024},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.5113999843597412},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5045999884605408},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.483599990606308},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44609999656677246},{"id":"https://openalex.org/C141898687","wikidata":"https://www.wikidata.org/wiki/Q1501997","display_name":"Hausdorff distance","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4163999855518341},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.39730000495910645},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.367900013923645},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.36079999804496765},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.349700003862381},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.29789999127388},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.2574999928474426},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.25690001249313354},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.51584/ijrias.2026.11080007","doi":"https://doi.org/10.51584/ijrias.2026.11080007","is_oa":true,"landing_page_url":"https://doi.org/10.51584/ijrias.2026.11080007","pdf_url":"https://rsisinternational.org/journals/ijrias/uploads/vol11-iss8-pg125-139-202608_pdf.pdf","source":{"id":"https://openalex.org/S4210227786","display_name":"International Journal of Research and Innovation in Applied Science","issn_l":"2454-6194","issn":["2454-6194"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"International Journal of Research and Innovation in Applied Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.51584/ijrias.2026.11080007","doi":"https://doi.org/10.51584/ijrias.2026.11080007","is_oa":true,"landing_page_url":"https://doi.org/10.51584/ijrias.2026.11080007","pdf_url":"https://rsisinternational.org/journals/ijrias/uploads/vol11-iss8-pg125-139-202608_pdf.pdf","source":{"id":"https://openalex.org/S4210227786","display_name":"International Journal of Research and Innovation in Applied Science","issn_l":"2454-6194","issn":["2454-6194"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"International Journal of Research and Innovation in Applied Science","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8560000061988831,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204454020.pdf","grobid_xml":"https://content.openalex.org/works/W7204454020.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2395611524","https://openalex.org/W2432481613","https://openalex.org/W2533800772","https://openalex.org/W2592691248","https://openalex.org/W2741891296","https://openalex.org/W2798122215","https://openalex.org/W2809470868","https://openalex.org/W2883311907","https://openalex.org/W2884436604","https://openalex.org/W2962878449","https://openalex.org/W2963091558","https://openalex.org/W2964227007","https://openalex.org/W3010659930","https://openalex.org/W3014974815","https://openalex.org/W3094502228","https://openalex.org/W3127751679","https://openalex.org/W3132503749","https://openalex.org/W3170544306","https://openalex.org/W4212875960","https://openalex.org/W4321232185","https://openalex.org/W4391109864"],"related_works":["https://openalex.org/W7163689706","https://openalex.org/W7163191725","https://openalex.org/W7125694310","https://openalex.org/W4402413488","https://openalex.org/W7117476553","https://openalex.org/W7128355047","https://openalex.org/W7168179426","https://openalex.org/W4388190812","https://openalex.org/W7128503637","https://openalex.org/W4402976645"],"abstract_inverted_index":{"Automatic":[0],"brain":[1,26,60,279],"Magnetic":[2],"Resonance":[3],"Imaging":[4],"(MRI)":[5],"analysis":[6,237,281],"plays":[7],"a":[8,49,71,84,125,152],"crucial":[9],"role":[10],"in":[11,166,277],"computer-aided":[12],"diagnosis,":[13],"treatment":[14],"planning,":[15],"and":[16,23,34,41,52,64,91,105,116,134,143,160,177,201,213,232,250,256,271,282],"disease":[17],"monitoring":[18],"by":[19,109],"enabling":[20],"accurate":[21],"delineation":[22],"identification":[24],"of":[25,129,132,138,155,158,163,173,189,220,224,227,230,234,242],"tumors.":[27],"However,":[28],"manual":[29],"segmentation":[30,63,122,255],"is":[31],"labor-intensive,":[32],"time-consuming,":[33],"susceptible":[35],"to":[36,78,112],"inter-observer":[37],"variability,":[38],"making":[39],"automated":[40,278],"reliable":[42],"methods":[43],"highly":[44],"desirable.":[45],"This":[46],"paper":[47],"proposes":[48],"Hybrid":[50],"U-Net++":[51,72],"Vision":[53,85],"Transformer":[54,86],"(ViT)":[55],"Fusion":[56],"framework":[57,150,263],"for":[58,187,268],"automatic":[59],"MRI":[61,280],"tumor":[62,114,182,269],"segmentation-guided":[65],"classification.":[66],"The":[67,121,180,205,253],"proposed":[68,149,244,262],"architecture":[69,246],"employs":[70],"encoder":[73],"with":[74,274],"nested":[75],"skip":[76],"connections":[77],"extract":[79],"hierarchical":[80],"multi-scale":[81],"features,":[82],"while":[83],"captures":[87],"long-range":[88],"spatial":[89],"dependencies":[90],"global":[92],"contextual":[93],"information":[94],"through":[95],"self-attention":[96],"mechanisms.":[97],"A":[98],"multi-stage":[99,174],"feature":[100,175,195],"fusion":[101,176],"strategy":[102],"integrates":[103],"convolutional":[104],"transformer":[106],"representations,":[107],"complemented":[108],"boundary-aware":[110,178],"refinement":[111],"improve":[113],"localization":[115,270],"preserve":[117],"fine":[118],"anatomical":[119],"details.":[120],"model":[123],"achieved":[124],"training":[126],"Dice":[127,136,153],"score":[128,137,154],"95.6%,":[130],"IoU":[131,157],"91.4%,":[133],"validation":[135],"92.6%,":[139],"demonstrating":[140],"stable":[141],"convergence":[142],"strong":[144],"generalization":[145],"capability.":[146],"Furthermore,":[147],"the":[148,167,171,240,243,261],"attained":[151],"95.8%,":[156],"92.1%,":[159],"Hausdorff":[161],"Distance":[162],"3.35":[164],"mm":[165],"ablation":[168],"analysis,":[169],"confirming":[170],"effectiveness":[172,241],"refinement.":[179],"segmented":[181],"regions":[183],"are":[184],"subsequently":[185],"utilized":[186],"Region":[188],"Interest":[190],"(ROI)-based":[191],"classification":[192,206,257],"using":[193],"deep":[194],"extraction,":[196],"Global":[197],"Average":[198],"Pooling":[199],"(GAP),":[200],"fully":[202],"connected":[203],"layers.":[204],"module":[207],"distinguishes":[208],"glioma,":[209],"meningioma,":[210],"pituitary":[211],"tumor,":[212],"no-tumor":[214],"cases,":[215],"achieving":[216],"an":[217,265],"overall":[218],"accuracy":[219],"97.55%,":[221],"macro-average":[222],"precision":[223],"97.13%,":[225],"recall":[226],"97.50%,":[228],"F1-score":[229],"97.31%,":[231],"specificity":[233],"98.40%.":[235],"Comparative":[236],"further":[238],"demonstrates":[239],"hybrid":[245],"over":[247],"conventional":[248],"CNN-based":[249],"transformer-based":[251],"approaches.":[252],"combined":[254],"results":[258],"indicate":[259],"that":[260],"provides":[264],"effective":[266],"approach":[267],"tumor-type":[272],"identification,":[273],"potential":[275],"applicability":[276],"clinical":[283],"decision":[284],"support.":[285]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-08-28T00:00:00"}