{"id":"https://openalex.org/W4387581304","doi":"https://doi.org/10.3390/sym15101912","title":"A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism","display_name":"A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism","publication_year":2023,"publication_date":"2023-10-12","ids":{"openalex":"https://openalex.org/W4387581304","doi":"https://doi.org/10.3390/sym15101912"},"language":"en","primary_location":{"id":"doi:10.3390/sym15101912","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym15101912","pdf_url":"https://www.mdpi.com/2073-8994/15/10/1912/pdf?version=1697121221","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/15/10/1912/pdf?version=1697121221","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102023423","display_name":"Zia-ur Rahman","orcid":"https://orcid.org/0000-0001-8233-567X"},"institutions":[{"id":"https://openalex.org/I10011244","display_name":"Huanggang Normal University","ror":"https://ror.org/007gf6e19","country_code":"CN","type":"education","lineage":["https://openalex.org/I10011244"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziaur Rahman","raw_affiliation_strings":["School of Computer, Huanggang Normal University, Huanggang 438000, China"],"raw_orcid":"https://orcid.org/0000-0001-8233-567X","affiliations":[{"raw_affiliation_string":"School of Computer, Huanggang Normal University, Huanggang 438000, China","institution_ids":["https://openalex.org/I10011244"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087735248","display_name":"Ruihong Zhang","orcid":"https://orcid.org/0000-0002-1541-8835"},"institutions":[{"id":"https://openalex.org/I10011244","display_name":"Huanggang Normal University","ror":"https://ror.org/007gf6e19","country_code":"CN","type":"education","lineage":["https://openalex.org/I10011244"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruihong Zhang","raw_affiliation_strings":["School of Computer, Huanggang Normal University, Huanggang 438000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer, Huanggang Normal University, Huanggang 438000, China","institution_ids":["https://openalex.org/I10011244"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026003182","display_name":"Jameel Ahmed Bhutto","orcid":"https://orcid.org/0000-0003-4896-7205"},"institutions":[{"id":"https://openalex.org/I10011244","display_name":"Huanggang Normal University","ror":"https://ror.org/007gf6e19","country_code":"CN","type":"education","lineage":["https://openalex.org/I10011244"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jameel Ahmed Bhutto","raw_affiliation_strings":["School of Computer, Huanggang Normal University, Huanggang 438000, China"],"raw_orcid":"https://orcid.org/0000-0003-4896-7205","affiliations":[{"raw_affiliation_string":"School of Computer, Huanggang Normal University, Huanggang 438000, China","institution_ids":["https://openalex.org/I10011244"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5087735248"],"corresponding_institution_ids":["https://openalex.org/I10011244"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":2.5043,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.91408684,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"15","issue":"10","first_page":"1912","last_page":"1912"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9998000264167786,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9994000196456909,"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/computer-science","display_name":"Computer science","score":0.7631587982177734},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7048551440238953},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6620760560035706},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5674123167991638},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5485251545906067},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5087197422981262},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43299925327301025},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.42441830039024353},{"id":"https://openalex.org/keywords/information-flow","display_name":"Information flow","score":0.4236096143722534},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3771635591983795},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.16010883450508118}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7631587982177734},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7048551440238953},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6620760560035706},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5674123167991638},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5485251545906067},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5087197422981262},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43299925327301025},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.42441830039024353},{"id":"https://openalex.org/C2779136372","wikidata":"https://www.wikidata.org/wiki/Q10283002","display_name":"Information flow","level":2,"score":0.4236096143722534},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3771635591983795},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.16010883450508118},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/sym15101912","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym15101912","pdf_url":"https://www.mdpi.com/2073-8994/15/10/1912/pdf?version=1697121221","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:4fa0824ca9d74650ac0d17f549d11ff0","is_oa":false,"landing_page_url":"https://doaj.org/article/4fa0824ca9d74650ac0d17f549d11ff0","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 15, Iss 10, p 1912 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/sym15101912","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym15101912","pdf_url":"https://www.mdpi.com/2073-8994/15/10/1912/pdf?version=1697121221","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5863677358","display_name":null,"funder_award_id":"2042022007","funder_id":"https://openalex.org/F4320326545","funder_display_name":"Huanggang Normal University"}],"funders":[{"id":"https://openalex.org/F4320326545","display_name":"Huanggang Normal University","ror":"https://ror.org/007gf6e19"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4387581304.pdf"},"referenced_works_count":61,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2194775991","https://openalex.org/W2464708700","https://openalex.org/W2508741746","https://openalex.org/W2560023338","https://openalex.org/W2613456556","https://openalex.org/W2752782242","https://openalex.org/W2791575870","https://openalex.org/W2884436604","https://openalex.org/W2884585870","https://openalex.org/W2891511539","https://openalex.org/W2905011448","https://openalex.org/W2907750714","https://openalex.org/W2911355517","https://openalex.org/W2923997689","https://openalex.org/W2928133111","https://openalex.org/W2955058313","https://openalex.org/W2963046541","https://openalex.org/W2963091558","https://openalex.org/W2964189376","https://openalex.org/W2979967919","https://openalex.org/W3017153481","https://openalex.org/W3030952871","https://openalex.org/W3034920607","https://openalex.org/W3037117622","https://openalex.org/W3047447646","https://openalex.org/W3090974769","https://openalex.org/W3097086372","https://openalex.org/W3114814504","https://openalex.org/W3136933864","https://openalex.org/W3141170179","https://openalex.org/W3142919922","https://openalex.org/W3143351114","https://openalex.org/W3144727859","https://openalex.org/W3167784297","https://openalex.org/W3184219099","https://openalex.org/W3191712298","https://openalex.org/W3203480968","https://openalex.org/W3203841574","https://openalex.org/W3203950382","https://openalex.org/W4210786716","https://openalex.org/W4224248334","https://openalex.org/W4226244266","https://openalex.org/W4280638596","https://openalex.org/W4281572218","https://openalex.org/W4282839986","https://openalex.org/W4283080861","https://openalex.org/W4292849097","https://openalex.org/W4293546472","https://openalex.org/W4296640326","https://openalex.org/W4307552305","https://openalex.org/W4312069086","https://openalex.org/W4313889093","https://openalex.org/W4366287486","https://openalex.org/W4375953500","https://openalex.org/W4385697257","https://openalex.org/W4387437521","https://openalex.org/W6779616223","https://openalex.org/W6787268236","https://openalex.org/W6810545759"],"related_works":["https://openalex.org/W2560215812","https://openalex.org/W2949601986","https://openalex.org/W4375867731","https://openalex.org/W2788972299","https://openalex.org/W2521347458","https://openalex.org/W2498789492","https://openalex.org/W2729981612","https://openalex.org/W4233449973","https://openalex.org/W3209312100","https://openalex.org/W2972212393"],"abstract_inverted_index":{"The":[0,238],"symmetrical":[1,127,166],"segmentation":[2,73,222,250],"of":[3,25,59,109,115,119,178,251],"brain":[4,52,95,121,206,220,253],"tumor":[5,53,96,221,254],"images":[6,255],"is":[7,41,170],"crucial":[8],"for":[9,50,80,94],"both":[10,60,136],"clinical":[11],"diagnosis":[12],"and":[13,32,55,62,139,180,190,224,227,235],"computer-aided":[14],"prognosis.":[15],"Traditional":[16],"manual":[17],"methods":[18],"are":[19],"not":[20],"only":[21],"asymmetrical":[22,258],"in":[23,72,135,203,248,260],"terms":[24],"efficiency":[26],"but":[27],"also":[28],"prone":[29],"to":[30,38,85,187],"errors":[31],"lengthy":[33],"processing.":[34],"A":[35],"significant":[36],"barrier":[37],"the":[39,42,46,56,70,102,116,132,137,152,159,185,200,211,249],"process":[40],"complex":[43],"interplay":[44],"between":[45],"deep":[47,143],"learning":[48],"network":[49,160,186],"MRI":[51,120,205,252],"imaging":[54],"harmonious":[57,107,196],"compound":[58],"local":[61,179],"global":[63,181],"feature":[64],"information,":[65],"which":[66],"can":[67],"throw":[68],"off":[69],"balance":[71,149],"accuracy.":[74],"Addressing":[75],"this":[76,86],"asymmetry":[77],"becomes":[78],"essential":[79,192],"precise":[81],"diagnosis.":[82],"In":[83],"answer":[84],"challenge,":[87],"we":[88,123,146],"introduce":[89],"a":[90,106,126,148,165,175],"balanced,":[91],"end-to-end":[92],"solution":[93],"segmentation,":[97],"incorporating":[98],"modifications":[99],"that":[100,150,241],"mirror":[101],"U-Net":[103],"architecture,":[104],"ensuring":[105],"flow":[108],"information.":[110],"Beginning":[111],"with":[112,142],"symmetric":[113],"enhancement":[114],"visual":[117],"quality":[118],"images,":[122],"then":[124],"apply":[125],"residual":[128,144],"structure.":[129],"By":[130],"replacing":[131],"convolutional":[133],"modules":[134],"encoder":[138],"decoder":[140],"sections":[141],"modules,":[145],"establish":[147],"counters":[151],"vanishing":[153],"gradient":[154],"problem":[155],"commonly":[156],"faced":[157],"when":[158],"depth":[161],"increases.":[162],"Following":[163],"this,":[164],"threefold":[167],"attention":[168],"block":[169],"integrated.":[171],"This":[172,195],"addition":[173],"ensures":[174],"balanced":[176,243],"fusion":[177],"image":[182,193],"features,":[183],"fine-tuning":[184],"symmetrically":[188],"discern":[189],"learn":[191],"characteristics.":[194],"integration":[197],"remarkably":[198],"amplifies":[199],"network\u2019s":[201],"precision":[202],"segmenting":[204],"tumors.":[207],"We":[208],"further":[209],"validate":[210],"equilibrium":[212],"achieved":[213],"by":[214,228],"our":[215,230,242],"proposed":[216],"model":[217,231],"using":[218],"three":[219],"datasets":[223],"four":[225],"metrics":[226],"juxtaposing":[229],"against":[232],"21":[233],"traditional":[234],"learning-based":[236],"counterparts.":[237],"results":[239],"confirm":[240],"approach":[244],"significantly":[245],"elevates":[246],"performance":[247],"without":[256],"an":[257],"increase":[259],"computational":[261],"time.":[262]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
