{"id":"https://openalex.org/W4400905281","doi":"https://doi.org/10.1109/access.2024.3432803","title":"Inv-AlxVGGNets: Cervical Spine Disease Classification Using Concatenated Involutional Neural Networks With Residual Net","display_name":"Inv-AlxVGGNets: Cervical Spine Disease Classification Using Concatenated Involutional Neural Networks With Residual Net","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4400905281","doi":"https://doi.org/10.1109/access.2024.3432803"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3432803","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3432803","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":null,"license_id":null,"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.2024.3432803","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067026203","display_name":"Biniyam Mulugeta Abuhayi","orcid":"https://orcid.org/0000-0003-3717-5499"},"institutions":[{"id":"https://openalex.org/I8764889","display_name":"University of Gondar","ror":"https://ror.org/0595gz585","country_code":"ET","type":"education","lineage":["https://openalex.org/I8764889"]}],"countries":["ET"],"is_corresponding":false,"raw_author_name":"Biniyam Mulugeta Abuhayi","raw_affiliation_strings":["Department of Information Technology, University of Gondar, Gondar, Ethiopia"],"raw_orcid":"https://orcid.org/0000-0003-3717-5499","affiliations":[{"raw_affiliation_string":"Department of Information Technology, University of Gondar, Gondar, Ethiopia","institution_ids":["https://openalex.org/I8764889"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113153707","display_name":"Yohannes Agegnehu Bezabh","orcid":null},"institutions":[{"id":"https://openalex.org/I8764889","display_name":"University of Gondar","ror":"https://ror.org/0595gz585","country_code":"ET","type":"education","lineage":["https://openalex.org/I8764889"]}],"countries":["ET"],"is_corresponding":false,"raw_author_name":"Yohannes Agegnehu Bezabh","raw_affiliation_strings":["Department of Information Technology, University of Gondar, Gondar, Ethiopia"],"raw_orcid":"https://orcid.org/0000-0001-5175-1769","affiliations":[{"raw_affiliation_string":"Department of Information Technology, University of Gondar, Gondar, Ethiopia","institution_ids":["https://openalex.org/I8764889"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046075996","display_name":"Aleka Melese Ayalew","orcid":"https://orcid.org/0000-0001-5869-6029"},"institutions":[{"id":"https://openalex.org/I8764889","display_name":"University of Gondar","ror":"https://ror.org/0595gz585","country_code":"ET","type":"education","lineage":["https://openalex.org/I8764889"]}],"countries":["ET"],"is_corresponding":false,"raw_author_name":"Aleka Melese Ayalew","raw_affiliation_strings":["Department of Information Technology, University of Gondar, Gondar, Ethiopia"],"raw_orcid":"https://orcid.org/0000-0001-5869-6029","affiliations":[{"raw_affiliation_string":"Department of Information Technology, University of Gondar, Gondar, Ethiopia","institution_ids":["https://openalex.org/I8764889"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.6511,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.64663198,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"12","issue":null,"first_page":"102188","last_page":"102201"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11518","display_name":"Cervical and Thoracic Myelopathy","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T10238","display_name":"Spine and Intervertebral Disc Pathology","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/2734","display_name":"Pathology and Forensic Medicine"},"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/computer-science","display_name":"Computer science","score":0.8000705242156982},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7350599765777588},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.598461389541626},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5903940796852112},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4861888289451599},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.46575242280960083},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43666452169418335},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4227815568447113},{"id":"https://openalex.org/keywords/spondylolisthesis","display_name":"Spondylolisthesis","score":0.4197041690349579},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37365931272506714},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1635110080242157},{"id":"https://openalex.org/keywords/lumbar","display_name":"Lumbar","score":0.12488371133804321},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.12198436260223389},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.1130957305431366},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.10945218801498413}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8000705242156982},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7350599765777588},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.598461389541626},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5903940796852112},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4861888289451599},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.46575242280960083},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43666452169418335},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4227815568447113},{"id":"https://openalex.org/C2776623965","wikidata":"https://www.wikidata.org/wiki/Q973524","display_name":"Spondylolisthesis","level":3,"score":0.4197041690349579},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37365931272506714},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1635110080242157},{"id":"https://openalex.org/C44575665","wikidata":"https://www.wikidata.org/wiki/Q1293405","display_name":"Lumbar","level":2,"score":0.12488371133804321},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.12198436260223389},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.1130957305431366},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10945218801498413}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3432803","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3432803","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a68973dd63b943f09122291de6f422c9","is_oa":true,"landing_page_url":"https://doaj.org/article/a68973dd63b943f09122291de6f422c9","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":"IEEE Access, Vol 12, Pp 102188-102201 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3432803","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3432803","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5400000214576721,"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1996394238","https://openalex.org/W2017655060","https://openalex.org/W2038306701","https://openalex.org/W2147000212","https://openalex.org/W3118451526","https://openalex.org/W3177349073","https://openalex.org/W3196545708","https://openalex.org/W3206742932","https://openalex.org/W4281481919","https://openalex.org/W4293518224","https://openalex.org/W4295036246","https://openalex.org/W4313331083","https://openalex.org/W4313472890","https://openalex.org/W4319321252","https://openalex.org/W4320716607","https://openalex.org/W4327521065","https://openalex.org/W4327958873","https://openalex.org/W4328048428","https://openalex.org/W4353070801","https://openalex.org/W4360996879","https://openalex.org/W4361204230","https://openalex.org/W4361802861","https://openalex.org/W4362516016","https://openalex.org/W4362544918","https://openalex.org/W4362709188","https://openalex.org/W4366962764","https://openalex.org/W4367598955","https://openalex.org/W4377224238","https://openalex.org/W4379931504","https://openalex.org/W4382562485","https://openalex.org/W4382652576","https://openalex.org/W4382935295","https://openalex.org/W4383616257","https://openalex.org/W4384819851","https://openalex.org/W4385080490","https://openalex.org/W4391431385"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W4399254932","https://openalex.org/W2952813363"],"abstract_inverted_index":{"Cervical":[0],"spine":[1,103,231],"diseases,":[2],"encompassing":[3],"conditions":[4,24],"like":[5],"spondylolisthesis,":[6],"disc":[7],"degeneration,":[8],"and":[9,48,67,74,83,120,143,158,199,214],"cervical":[10,102,169,230],"spinal":[11,170],"stenosis,":[12],"stand":[13],"as":[14,126,128,224],"significant":[15],"contributors":[16],"to":[17,91],"global":[18],"disability.":[19],"Precise":[20],"classification":[21,105],"of":[22,42,115,139],"these":[23],"is":[25,205],"paramount":[26],"for":[27,167,228,236],"effective":[28],"medical":[29,96],"diagnosis.":[30],"This":[31,201],"paper":[32],"introduces":[33],"an":[34,150],"innovative":[35],"methodology":[36],"aimed":[37],"at":[38],"addressing":[39],"the":[40,113,155,161],"limitations":[41],"traditional":[43],"convolutional":[44],"neural":[45,72,86],"networks":[46,73,87],"(CNNs)":[47],"pretrained":[49,64],"models":[50],"in":[51,95,137,208,240],"this":[52],"domain.":[53],"We":[54],"propose":[55],"a":[56,100,174,225],"novel":[57],"approach":[58],"dubbed":[59],"Inv-AlxVGGNets,":[60],"which":[61],"leverages":[62],"concatenated":[63],"architectures":[65],"AlexNet":[66],"VGG,":[68],"augmented":[69],"by":[70],"involutional":[71,85],"residual":[75,123],"layers.":[76],"Unlike":[77],"conventional":[78,180],"CNNs":[79,181],"that":[80,179],"are":[81],"location-specific":[82],"channel-agnostic,":[84],"offer":[88],"enhanced":[89],"Adaptability":[90],"diverse":[92],"visual":[93],"patterns":[94],"images.":[97],"Focusing":[98],"on":[99,154,160],"four-class":[101],"disease":[104,171,232],"task":[106],"utilizing":[107],"MRI":[108],"images,":[109],"our":[110],"study":[111],"evaluates":[112],"performance":[114,136],"Inv-AlxVGGNets":[116,148,188,223],"(AlexNet":[117],"with":[118,122],"INN":[119],"VGG":[121],"layer":[124],"models)":[125],"well":[127],"machine":[129],"learning":[130],"algorithms.":[131],"Our":[132],"findings":[133],"demonstrate":[134],"superior":[135],"terms":[138],"accuracy,":[140],"precision,":[141],"recall,":[142],"AUC":[144],"ROC":[145],"values.":[146],"Notably,":[147],"achieves":[149],"impressive":[151],"98.73%":[152],"accuracy":[153],"testing":[156],"set":[157],"99.78%":[159],"training":[162],"set,":[163],"underscoring":[164],"its":[165],"potential":[166],"precise":[168,229],"classification.":[172],"In":[173],"comparative":[175],"analysis,":[176],"we":[177],"highlight":[178],"entail":[182],"over":[183],"133":[184],"million":[185,193],"parameters,":[186,194],"whereas":[187],"require":[189],"less":[190],"than":[191],"8":[192],"rendering":[195],"them":[196],"more":[197],"efficient":[198],"resource-friendly.":[200],"reduced":[202],"parameter":[203],"count":[204],"particularly":[206],"advantageous":[207],"resource-constrained":[209,241],"scenarios,":[210],"where":[211],"computational":[212],"resources":[213],"datasets":[215],"may":[216],"be":[217],"limited.":[218],"The":[219],"promising":[220],"results":[221],"position":[222],"valuable":[226],"tool":[227],"classification,":[233],"offering":[234],"implications":[235],"enhancing":[237],"patient":[238],"care":[239],"settings.":[242]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}