{"id":"https://openalex.org/W7116841738","doi":"https://doi.org/10.3390/info17010014","title":"PRA-Unet: Parallel Residual Attention U-Net for Real-Time Segmentation of Brain Tumors","display_name":"PRA-Unet: Parallel Residual Attention U-Net for Real-Time Segmentation of Brain Tumors","publication_year":2025,"publication_date":"2025-12-23","ids":{"openalex":"https://openalex.org/W7116841738","doi":"https://doi.org/10.3390/info17010014"},"language":"en","primary_location":{"id":"doi:10.3390/info17010014","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info17010014","pdf_url":"https://www.mdpi.com/2078-2489/17/1/14/pdf","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"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":"Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2078-2489/17/1/14/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121106149","display_name":"Ali Zakaria Lebani","orcid":null},"institutions":[{"id":"https://openalex.org/I4210106700","display_name":"Universit\u00e9 IBN Khaldoun Tiaret","ror":"https://ror.org/018jjaw76","country_code":"DZ","type":"education","lineage":["https://openalex.org/I4210106700"]}],"countries":["DZ"],"is_corresponding":true,"raw_author_name":"Ali Zakaria Lebani","raw_affiliation_strings":["Department of Computer Science, University of Tiaret, Tiaret 14000, Algeria","Laboratoire de G\u00e9nie Energ\u00e9tique et G\u00e9nie Informatique (L2GEGI), University of Tiaret, Tiaret 14000, Algeria"],"raw_orcid":"https://orcid.org/0009-0002-7509-7364","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Tiaret, Tiaret 14000, Algeria","institution_ids":["https://openalex.org/I4210106700"]},{"raw_affiliation_string":"Laboratoire de G\u00e9nie Energ\u00e9tique et G\u00e9nie Informatique (L2GEGI), University of Tiaret, Tiaret 14000, Algeria","institution_ids":["https://openalex.org/I4210106700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121087383","display_name":"Medjeded Merati","orcid":null},"institutions":[{"id":"https://openalex.org/I4210106700","display_name":"Universit\u00e9 IBN Khaldoun Tiaret","ror":"https://ror.org/018jjaw76","country_code":"DZ","type":"education","lineage":["https://openalex.org/I4210106700"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Medjeded Merati","raw_affiliation_strings":["Department of Computer Science, University of Tiaret, Tiaret 14000, Algeria","Laboratoire d\u2019Informatique et Mathematique (LIM), University of Tiaret, Tiaret 14000, Algeria"],"raw_orcid":"https://orcid.org/0000-0003-2525-6814","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Tiaret, Tiaret 14000, Algeria","institution_ids":["https://openalex.org/I4210106700"]},{"raw_affiliation_string":"Laboratoire d\u2019Informatique et Mathematique (LIM), University of Tiaret, Tiaret 14000, Algeria","institution_ids":["https://openalex.org/I4210106700"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5121070171","display_name":"Sa\u00efd Mahmoudi","orcid":null},"institutions":[{"id":"https://openalex.org/I130929987","display_name":"University of Mons","ror":"https://ror.org/02qnnz951","country_code":"BE","type":"education","lineage":["https://openalex.org/I130929987"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Sa\u00efd Mahmoudi","raw_affiliation_strings":["Department of Computer Science, University of Mons, 7000 Mons, Belgium"],"raw_orcid":"https://orcid.org/0000-0001-8272-9425","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Mons, 7000 Mons, Belgium","institution_ids":["https://openalex.org/I130929987"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5121106149"],"corresponding_institution_ids":["https://openalex.org/I4210106700"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.59664217,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"1","first_page":"14","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6449000239372253,"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":0.6449000239372253,"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":0.2451000064611435,"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":0.03799999877810478,"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/segmentation","display_name":"Segmentation","score":0.7310000061988831},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5985999703407288},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5703999996185303},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.54830002784729},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5044000148773193},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.49810001254081726},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49720001220703125},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4375999867916107},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4106000065803528}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8377000093460083},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7310000061988831},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5985999703407288},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5979999899864197},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5703999996185303},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.54830002784729},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5044000148773193},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.49810001254081726},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49720001220703125},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4375999867916107},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4106000065803528},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.40689998865127563},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.39010000228881836},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3898000121116638},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3675999939441681},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.36469998955726624},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.305400013923645},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C191172861","wikidata":"https://www.wikidata.org/wiki/Q7899321","display_name":"Upstream (networking)","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.2728999853134155},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.26969999074935913},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.25619998574256897},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/info17010014","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info17010014","pdf_url":"https://www.mdpi.com/2078-2489/17/1/14/pdf","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"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":"Information","raw_type":"journal-article"},{"id":"pmh:oai:orbi.umons.ac.be:20.500.12907/55390","is_oa":true,"landing_page_url":"https://orbi.umons.ac.be/handle/20.500.12907/55390","pdf_url":"https://orbi.umons.ac.be/bitstream/20.500.12907/55390/1/information-17-00014-v2%20_%20LEBANI.pdf","source":{"id":"https://openalex.org/S7407055454","display_name":"ORBi UMONS","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Information, 17 (1), 14 (2025-12-23)","raw_type":"peer reviewed"},{"id":"pmh:oai:doaj.org/article:aff61af4c7084c3aa60977c133cd09e9","is_oa":true,"landing_page_url":"https://doaj.org/article/aff61af4c7084c3aa60977c133cd09e9","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":"Information, Vol 17, Iss 1, p 14 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/info17010014","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info17010014","pdf_url":"https://www.mdpi.com/2078-2489/17/1/14/pdf","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"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":"Information","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7116841738.pdf","grobid_xml":"https://content.openalex.org/works/W7116841738.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1641498739","https://openalex.org/W1901129140","https://openalex.org/W1909740415","https://openalex.org/W2119821739","https://openalex.org/W2194775991","https://openalex.org/W2310992461","https://openalex.org/W2531409750","https://openalex.org/W2751069891","https://openalex.org/W2884436604","https://openalex.org/W2884585870","https://openalex.org/W2905338897","https://openalex.org/W2963163009","https://openalex.org/W2964309882","https://openalex.org/W2983817809","https://openalex.org/W3007268491","https://openalex.org/W3014641072","https://openalex.org/W3018852185","https://openalex.org/W3035358681","https://openalex.org/W3159481202","https://openalex.org/W3194031773","https://openalex.org/W4212875960","https://openalex.org/W4214893857","https://openalex.org/W4214897413","https://openalex.org/W4312815172","https://openalex.org/W4387163062","https://openalex.org/W4388486446","https://openalex.org/W4388677178","https://openalex.org/W4394911487","https://openalex.org/W4400347419","https://openalex.org/W4405817140","https://openalex.org/W4406039681","https://openalex.org/W4407671937","https://openalex.org/W4415463785"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,107,144,150],"increasing":[2],"prevalence":[3],"of":[4,112,193,197,203],"brain":[5,99,167],"tumors,":[6],"it":[7,161,223],"becomes":[8],"crucial":[9],"to":[10,27,74,130,163,242],"ensure":[11],"fast":[12,95],"and":[13,30,38,47,70,96,118,126,143,152,159,165,199,238,246],"reliable":[14],"segmentation":[15,25,34,216],"in":[16,215,233],"MRI":[17,113],"scans.":[18],"Medical":[19],"professionals":[20],"struggle":[21],"with":[22,78],"manual":[23],"tumor":[24,100],"due":[26],"its":[28],"exhausting":[29],"time-consuming":[31],"nature.":[32],"Automated":[33],"speeds":[35],"up":[36],"decision-making":[37],"diagnosis;":[39],"however,":[40],"achieving":[41],"an":[42,195],"optimal":[43],"balance":[44],"between":[45],"accuracy":[46,196],"computational":[48,178],"cost":[49],"remains":[50],"a":[51,88,103,190,200],"significant":[52],"challenge.":[53],"In":[54],"many":[55],"cases,":[56],"current":[57],"methods":[58],"trade":[59],"speed":[60,202,237],"for":[61,94,227],"accuracy,":[62],"or":[63],"vice":[64],"versa,":[65],"consuming":[66],"substantial":[67],"computing":[68,220],"power":[69],"making":[71,160],"them":[72],"difficult":[73],"use":[75],"on":[76,229],"devices":[77,232],"limited":[79],"resources.":[80],"To":[81],"address":[82],"this":[83],"issue,":[84],"we":[85],"present":[86],"PRA-UNet,":[87],"lightweight":[89,230],"deep":[90],"learning":[91],"model":[92],"optimized":[93],"accurate":[97],"2D":[98,105],"segmentation.":[101],"Using":[102],"single":[104],"input,":[106],"architecture":[108],"processes":[109],"four":[110],"types":[111],"scans":[114],"(FLAIR,":[115],"T1,":[116],"T1c,":[117],"T2).":[119],"The":[120,137,169,183],"encoder":[121],"uses":[122,171],"inverted":[123],"residual":[124,128],"blocks":[125,129],"bottleneck":[127],"capture":[131],"features":[132],"at":[133],"different":[134],"scales":[135],"effectively.":[136],"Convolutional":[138],"Block":[139],"Attention":[140,146],"Module":[141,147],"(CBAM)":[142],"Spatial":[145],"(SAM)":[148],"improve":[149],"bridge":[151],"skip":[153],"connections":[154],"by":[155],"refining":[156],"feature":[157],"maps":[158],"easier":[162],"detect":[164],"localize":[166],"tumors.":[168],"decoder":[170],"depthwise":[172],"separable":[173],"convolutions,":[174],"which":[175],"significantly":[176],"reduce":[177],"costs":[179],"without":[180],"degrading":[181],"accuracy.":[182],"BraTS2020":[184],"dataset":[185],"shows":[186],"that":[187],"PRA-UNet":[188,211],"achieves":[189],"Dice":[191],"score":[192],"95.71%,":[194],"99.61%,":[198],"processing":[201],"60":[204],"ms":[205],"per":[206],"image,":[207],"enabling":[208],"real-time":[209],"analysis.":[210],"outperforms":[212],"other":[213],"models":[214],"while":[217],"requiring":[218],"less":[219],"power,":[221],"suggesting":[222],"could":[224],"be":[225],"suitable":[226],"deployment":[228],"edge":[231],"clinical":[234],"settings.":[235],"Its":[236],"reliability":[239],"enable":[240],"radiologists":[241],"diagnose":[243],"tumors":[244],"quickly":[245],"accurately,":[247],"enhancing":[248],"practical":[249],"medical":[250],"applications.":[251]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-12-23T00:00:00"}