{"id":"https://openalex.org/W4285608075","doi":"https://doi.org/10.48550/arxiv.2207.07117","title":"A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT","display_name":"A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT","publication_year":2022,"publication_date":"2022-06-15","ids":{"openalex":"https://openalex.org/W4285608075","doi":"https://doi.org/10.48550/arxiv.2207.07117"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2207.07117","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.07117","pdf_url":"https://arxiv.org/pdf/2207.07117","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2207.07117","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050755564","display_name":"Justin Liu","orcid":"https://orcid.org/0000-0002-5338-6491"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liu, Justin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5050755564"],"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9563999772071838,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory 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/preprocessor","display_name":"Preprocessor","score":0.7385866045951843},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.726652979850769},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6422223448753357},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.640127420425415},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6184933185577393},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4823957681655884},{"id":"https://openalex.org/keywords/severe-acute-respiratory-syndrome-coronavirus-2","display_name":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","score":0.4391598105430603},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4273498058319092},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.29865074157714844},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.265524685382843},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.20405367016792297},{"id":"https://openalex.org/keywords/infectious-disease","display_name":"Infectious disease (medical specialty)","score":0.09748417139053345},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.09724608063697815}],"concepts":[{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.7385866045951843},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.726652979850769},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6422223448753357},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.640127420425415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6184933185577393},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4823957681655884},{"id":"https://openalex.org/C3007834351","wikidata":"https://www.wikidata.org/wiki/Q82069695","display_name":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","level":5,"score":0.4391598105430603},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4273498058319092},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.29865074157714844},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.265524685382843},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.20405367016792297},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.09748417139053345},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.09724608063697815}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2207.07117","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.07117","pdf_url":"https://arxiv.org/pdf/2207.07117","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2207.07117","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2207.07117","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2207.07117","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.07117","pdf_url":"https://arxiv.org/pdf/2207.07117","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4206669628","https://openalex.org/W4224279380","https://openalex.org/W4205317059","https://openalex.org/W3176864053","https://openalex.org/W3198183218","https://openalex.org/W4206651655","https://openalex.org/W4206548596","https://openalex.org/W4292098121","https://openalex.org/W4210433452","https://openalex.org/W3036314732"],"abstract_inverted_index":{"In":[0],"a":[1,12,80,108,144,181],"worldwide":[2],"health":[3],"crisis":[4],"as":[5,7,24,221],"exigent":[6],"COVID-19,":[8],"there":[9],"has":[10,62],"become":[11],"pressing":[13],"need":[14],"for":[15],"rapid,":[16],"reliable":[17],"diagnostics.":[18],"Currently,":[19],"popular":[20],"testing":[21],"methods":[22],"such":[23],"reverse":[25],"transcription":[26],"polymerase":[27],"chain":[28],"reaction":[29],"(RT-PCR)":[30],"can":[31],"have":[32],"high":[33],"false":[34],"negative":[35],"rates.":[36],"Consequently,":[37],"COVID-19":[38,89,199,212],"patients":[39],"are":[40],"not":[41],"accurately":[42],"identified":[43],"nor":[44],"treated":[45],"quickly":[46],"enough":[47],"to":[48,78,125,142,157,198],"prevent":[49],"transmission":[50],"of":[51,58,73,88,110,178,183],"the":[52,55,84,95,138,189],"virus.":[53],"However,":[54],"recent":[56],"rise":[57],"medical":[59],"CT":[60,67,92,113,201],"data":[61],"presented":[63],"promising":[64],"avenues,":[65],"since":[66],"manifestations":[68],"contain":[69],"key":[70],"characteristics":[71],"indicative":[72],"COVID-19.":[74],"This":[75],"study":[76,100],"aimed":[77],"take":[79],"novel":[81],"approach":[82],"in":[83,98],"machine":[85],"learning-based":[86],"detection":[87],"from":[90,103],"chest":[91,112,200],"scans.":[93],"First,":[94],"dataset":[96],"utilized":[97],"this":[99],"was":[101,134],"derived":[102],"three":[104],"major":[105],"sources,":[106],"comprising":[107],"total":[109],"17,698":[111],"slices":[114],"across":[115],"923":[116],"patient":[117,234],"cases.":[118],"Image":[119],"preprocessing":[120],"algorithms":[121],"were":[122,155],"then":[123],"developed":[124],"reduce":[126],"noise":[127],"by":[128,162],"excluding":[129],"irrelevant":[130],"features.":[131],"Transfer":[132],"learning":[133,207],"also":[135],"implemented":[136],"with":[137],"EfficientNetB7":[139],"pre-trained":[140],"model":[141,160,173,190],"provide":[143,209,229],"backbone":[145],"architecture":[146],"and":[147,166,180,203,237],"save":[148],"computational":[149],"resources.":[150],"Lastly,":[151],"several":[152],"explainability":[153],"techniques":[154],"leveraged":[156],"qualitatively":[158],"validate":[159],"performance":[161],"localizing":[163],"infected":[164],"regions":[165],"highlighting":[167],"fine-grained":[168],"pixel":[169],"details.":[170],"The":[171],"proposed":[172],"attained":[174],"an":[175,222],"overall":[176],"accuracy":[177],"0.927":[179],"sensitivity":[182],"0.958.":[184],"Explainability":[185],"measures":[186],"showed":[187],"that":[188,214],"correctly":[191],"distinguished":[192],"between":[193],"relevant,":[194],"critical":[195],"features":[196],"pertaining":[197],"images":[202],"normal":[204],"controls.":[205],"Deep":[206],"frameworks":[208],"efficient,":[210],"human-interpretable":[211],"diagnostics":[213],"could":[215],"complement":[216],"radiologist":[217],"decisions":[218],"or":[219],"serve":[220],"alternative":[223],"screening":[224],"tool.":[225],"Future":[226],"endeavors":[227],"may":[228],"insight":[230],"into":[231],"infection":[232],"severity,":[233],"risk":[235],"stratification,":[236],"prognosis.":[238]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2022-07-16T00:00:00"}
