{"id":"https://openalex.org/W7117129099","doi":"https://doi.org/10.48550/arxiv.2512.18573","title":"Placenta Accreta Spectrum Detection Using an MRI-based Hybrid CNN-Transformer Model","display_name":"Placenta Accreta Spectrum Detection Using an MRI-based Hybrid CNN-Transformer Model","publication_year":2025,"publication_date":"2025-12-21","ids":{"openalex":"https://openalex.org/W7117129099","doi":"https://doi.org/10.48550/arxiv.2512.18573"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.18573","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.18573","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2512.18573","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121234029","display_name":"Sumaiya Ali","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ali, Sumaiya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064394288","display_name":"Areej Alhothali","orcid":"https://orcid.org/0000-0001-9727-0178"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alhothali, Areej","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032990970","display_name":"Ohoud Alzamzami","orcid":"https://orcid.org/0000-0002-6555-8166"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alzamzami, Ohoud","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033224629","display_name":"Sameera F.M. AlBasri","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Albasri, Sameera","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041873015","display_name":"Ahmed H. Abduljabbar","orcid":"https://orcid.org/0000-0003-0508-8657"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abduljabbar, Ahmed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5121131873","display_name":"Muhammad Alwazzan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alwazzan, Muhammad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11389","display_name":"Maternal and fetal healthcare","score":0.4740000069141388,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/T11389","display_name":"Maternal and fetal healthcare","score":0.4740000069141388,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/T12552","display_name":"Fetal and Pediatric Neurological Disorders","score":0.2117999941110611,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/T10290","display_name":"Pregnancy and preeclampsia studies","score":0.14100000262260437,"subfield":{"id":"https://openalex.org/subfields/2729","display_name":"Obstetrics and Gynecology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/placenta-accreta","display_name":"Placenta accreta","score":0.6399999856948853},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.546500027179718},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5259000062942505},{"id":"https://openalex.org/keywords/gold-standard","display_name":"Gold standard (test)","score":0.3921000063419342},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34619998931884766},{"id":"https://openalex.org/keywords/data-consistency","display_name":"Data consistency","score":0.3418999910354614},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.3301999866962433}],"concepts":[{"id":"https://openalex.org/C2779116671","wikidata":"https://www.wikidata.org/wiki/Q517856","display_name":"Placenta accreta","level":5,"score":0.6399999856948853},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5802000164985657},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.546500027179718},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5259000062942505},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48590001463890076},{"id":"https://openalex.org/C40993552","wikidata":"https://www.wikidata.org/wiki/Q514654","display_name":"Gold standard (test)","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.35499998927116394},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34619998931884766},{"id":"https://openalex.org/C93361087","wikidata":"https://www.wikidata.org/wiki/Q4426698","display_name":"Data consistency","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3257000148296356},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32269999384880066},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.30300000309944153},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2827000021934509},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.2718000113964081},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C3019007443","wikidata":"https://www.wikidata.org/wiki/Q568742","display_name":"3d model","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C3020132585","wikidata":"https://www.wikidata.org/wiki/Q2671652","display_name":"Diagnostic accuracy","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2583000063896179}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.18573","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.18573","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2512.18573","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.18573","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.5772667527198792,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Placenta":[0],"Accreta":[1],"Spectrum":[2],"(PAS)":[3],"is":[4,44],"a":[5,30,52,60,76,106,122,130,138],"serious":[6],"obstetric":[7],"condition":[8],"that":[9],"can":[10],"be":[11],"challenging":[12],"to":[13,21,55,65,133,142],"diagnose":[14],"with":[15,105],"Magnetic":[16],"Resonance":[17],"Imaging":[18],"(MRI)":[19],"due":[20],"variability":[22],"in":[23,46],"radiologists'":[24],"interpretations.":[25],"To":[26],"overcome":[27],"this":[28,47],"challenge,":[29],"hybrid":[31,118],"3D":[32,53,61,84],"deep":[33,85],"learning":[34,86],"model":[35,50,66,100],"for":[36,91],"automated":[37],"PAS":[38,156],"detection":[39],"from":[40],"volumetric":[41],"MRI":[42,81],"scans":[43],"proposed":[45],"study.":[48],"The":[49,126],"integrates":[51],"DenseNet121":[54],"capture":[56],"local":[57],"features":[58],"and":[59,73,148,153],"Vision":[62],"Transformer":[63],"(ViT)":[64],"global":[67],"spatial":[68],"context.":[69],"It":[70],"was":[71],"developed":[72],"evaluated":[74,90],"on":[75],"retrospective":[77],"dataset":[78],"of":[79,110,117,155],"1,133":[80],"volumes.":[82],"Multiple":[83],"architectures":[87],"were":[88],"also":[89],"comparison.":[92],"On":[93],"an":[94],"independent":[95],"test":[96],"set,":[97],"the":[98,102,115,151],"DenseNet121-ViT":[99],"achieved":[101],"highest":[103],"performance":[104,128],"five-run":[107],"average":[108],"accuracy":[109,152],"84.3%.":[111],"These":[112],"results":[113],"highlight":[114],"strength":[116],"CNN-Transformer":[119],"models":[120],"as":[121],"computer-aided":[123],"diagnosis":[124],"tool.":[125],"model's":[127],"demonstrates":[129],"clear":[131],"potential":[132],"assist":[134],"radiologists":[135],"by":[136],"providing":[137],"robust":[139],"decision":[140],"support":[141],"improve":[143],"diagnostic":[144],"consistency":[145],"across":[146],"interpretations,":[147],"ultimately":[149],"enhance":[150],"timeliness":[154],"diagnosis.":[157]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-12-24T00:00:00"}