{"id":"https://openalex.org/W4285813924","doi":"https://doi.org/10.1109/iwcmc55113.2022.9824423","title":"Toward an Efficient Deep Learning Model for Lung pathologies Detection In X-ray Images","display_name":"Toward an Efficient Deep Learning Model for Lung pathologies Detection In X-ray Images","publication_year":2022,"publication_date":"2022-05-30","ids":{"openalex":"https://openalex.org/W4285813924","doi":"https://doi.org/10.1109/iwcmc55113.2022.9824423"},"language":"en","primary_location":{"id":"doi:10.1109/iwcmc55113.2022.9824423","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc55113.2022.9824423","pdf_url":null,"source":{"id":"https://openalex.org/S4363605313","display_name":"2022 International Wireless Communications and Mobile Computing (IWCMC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Wireless Communications and Mobile Computing (IWCMC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031951295","display_name":"Abdelbaki Souid","orcid":"https://orcid.org/0000-0002-4820-231X"},"institutions":[{"id":"https://openalex.org/I68916915","display_name":"University of Gab\u00e8s","ror":"https://ror.org/022efad20","country_code":"TN","type":"education","lineage":["https://openalex.org/I68916915"]}],"countries":["TN"],"is_corresponding":true,"raw_author_name":"Abdelbaki SOUID","raw_affiliation_strings":["University of Gabes,MACS Laboratory, RL16ES22, ENIG,,Gabes,Tunisia","MACS Laboratory, RL16ES22, ENIG,, University of Gabes, Gabes, Tunisia"],"affiliations":[{"raw_affiliation_string":"University of Gabes,MACS Laboratory, RL16ES22, ENIG,,Gabes,Tunisia","institution_ids":["https://openalex.org/I68916915"]},{"raw_affiliation_string":"MACS Laboratory, RL16ES22, ENIG,, University of Gabes, Gabes, Tunisia","institution_ids":["https://openalex.org/I68916915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083157722","display_name":"Nizar Sakli","orcid":null},"institutions":[{"id":"https://openalex.org/I68916915","display_name":"University of Gab\u00e8s","ror":"https://ror.org/022efad20","country_code":"TN","type":"education","lineage":["https://openalex.org/I68916915"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Nizar SAKLI","raw_affiliation_strings":["University of Gabes,MACS Laboratory, RL16ES22, ENIG,,Gabes,Tunisia","EITA Consulting, 5 rue du chants des oiseaux, Montesson, France","MACS Laboratory, RL16ES22, ENIG,, University of Gabes, Gabes, Tunisia"],"affiliations":[{"raw_affiliation_string":"University of Gabes,MACS Laboratory, RL16ES22, ENIG,,Gabes,Tunisia","institution_ids":["https://openalex.org/I68916915"]},{"raw_affiliation_string":"EITA Consulting, 5 rue du chants des oiseaux, Montesson, France","institution_ids":[]},{"raw_affiliation_string":"MACS Laboratory, RL16ES22, ENIG,, University of Gabes, Gabes, Tunisia","institution_ids":["https://openalex.org/I68916915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077504804","display_name":"H\u00e9di Sakli","orcid":"https://orcid.org/0000-0002-7702-6781"},"institutions":[{"id":"https://openalex.org/I68916915","display_name":"University of Gab\u00e8s","ror":"https://ror.org/022efad20","country_code":"TN","type":"education","lineage":["https://openalex.org/I68916915"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Hedi SAKLI","raw_affiliation_strings":["University of Gabes,MACS Laboratory, RL16ES22, ENIG,,Gabes,Tunisia","MACS Laboratory, RL16ES22, ENIG,, University of Gabes, Gabes, Tunisia","EITA Consulting, 5 rue du chants des oiseaux, Montesson, France"],"affiliations":[{"raw_affiliation_string":"University of Gabes,MACS Laboratory, RL16ES22, ENIG,,Gabes,Tunisia","institution_ids":["https://openalex.org/I68916915"]},{"raw_affiliation_string":"MACS Laboratory, RL16ES22, ENIG,, University of Gabes, Gabes, Tunisia","institution_ids":["https://openalex.org/I68916915"]},{"raw_affiliation_string":"EITA Consulting, 5 rue du chants des oiseaux, Montesson, France","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5031951295"],"corresponding_institution_ids":["https://openalex.org/I68916915"],"apc_list":null,"apc_paid":null,"fwci":3.1273,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.95868882,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1028","last_page":"1033"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9995999932289124,"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":0.9995999932289124,"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":0.991100013256073,"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":0.9900000095367432,"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/deep-learning","display_name":"Deep learning","score":0.836971640586853},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.765151858329773},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7140311598777771},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6963781118392944},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6059482097625732},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5855847001075745},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.5241553783416748},{"id":"https://openalex.org/keywords/radiography","display_name":"Radiography","score":0.47920092940330505},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44806164503097534},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.32165414094924927},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.22055765986442566}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.836971640586853},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.765151858329773},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7140311598777771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6963781118392944},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6059482097625732},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5855847001075745},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.5241553783416748},{"id":"https://openalex.org/C36454342","wikidata":"https://www.wikidata.org/wiki/Q245341","display_name":"Radiography","level":2,"score":0.47920092940330505},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44806164503097534},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.32165414094924927},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.22055765986442566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwcmc55113.2022.9824423","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcmc55113.2022.9824423","pdf_url":null,"source":{"id":"https://openalex.org/S4363605313","display_name":"2022 International Wireless Communications and Mobile Computing (IWCMC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Wireless Communications and Mobile Computing (IWCMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2019566532","https://openalex.org/W2028798134","https://openalex.org/W2054931962","https://openalex.org/W2108598243","https://openalex.org/W2163060476","https://openalex.org/W2322371438","https://openalex.org/W2557738935","https://openalex.org/W2559794190","https://openalex.org/W2611650229","https://openalex.org/W2772723798","https://openalex.org/W2798251715","https://openalex.org/W2805320571","https://openalex.org/W2884261459","https://openalex.org/W2888397986","https://openalex.org/W2897722020","https://openalex.org/W2901954625","https://openalex.org/W2949477454","https://openalex.org/W2955425717","https://openalex.org/W2962838801","https://openalex.org/W2963163009","https://openalex.org/W2963446712","https://openalex.org/W2979005616","https://openalex.org/W2986087452","https://openalex.org/W3101156210","https://openalex.org/W3102255912","https://openalex.org/W3188210243","https://openalex.org/W6725739302","https://openalex.org/W6749680723","https://openalex.org/W6884884380"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W3024479225","https://openalex.org/W4323287533","https://openalex.org/W3171371563","https://openalex.org/W2995680918"],"abstract_inverted_index":{"Medical":[0],"imaging":[1,32],"methods":[2],"identify":[3],"and":[4,18,65,95,106,121,159],"record":[5],"anomalies":[6],"in":[7,49,79,124],"the":[8,43,101,108,136,143,160],"human":[9],"body.":[10],"These":[11],"techniques":[12],"are":[13],"critical":[14],"for":[15,93,117],"assessing,":[16],"diagnosing,":[17],"treating":[19],"lung":[20,97],"illnesses.":[21],"Chest":[22],"radiography":[23],"(chest":[24],"X-ray)":[25],"is":[26,132],"a":[27,35,90,150],"low-cost":[28],"yet":[29],"effective":[30],"medical":[31],"technology.":[33],"However,":[34],"scarcity":[36],"of":[37,77,111,129,157,163],"trained":[38],"radiologists":[39],"may":[40],"significantly":[41],"restrict":[42],"technique's":[44],"use.":[45],"To":[46,99],"detect":[47],"abnormality":[48],"chest":[50],"radiographs,":[51],"emerging":[52],"technologies":[53],"such":[54],"as":[55,89],"deep":[56,68,112],"learning":[57,69],"should":[58],"be":[59],"applied":[60],"to":[61,85,134,148,168],"improve":[62],"diagnostic":[63,91],"performance":[64],"accuracy.":[66],"CNN-based":[67],"algorithms":[70],"have":[71],"made":[72],"significant":[73],"progress.":[74],"The":[75,139],"success":[76],"CNN":[78],"image":[80],"classification":[81],"has":[82],"led":[83],"researchers":[84],"investigate":[86],"its":[87],"utility":[88],"method":[92],"identifying":[94],"characterizing":[96],"diseases.":[98],"achieve":[100],"defined":[102],"goal,":[103],"we":[104],"leverage":[105],"extend":[107],"EfficientNet":[109],"family":[110],"artificial":[113],"neural":[114],"networks":[115],"knowns":[116],"their":[118],"high":[119],"accuracy":[120],"small":[122],"footprint":[123],"other":[125,174],"applications.":[126],"A":[127],"collection":[128],"three":[130],"datasets":[131],"used":[133],"train":[135],"proposed":[137,144],"approach.":[138],"results":[140],"show":[141],"that":[142],"approach":[145],"was":[146],"able":[147],"produce":[149],"high-quality":[151],"model,":[152],"with":[153],"an":[154],"overall":[155,161],"AUC":[156],"0.871,":[158],"sensitivity":[162],"79.4%,":[164],"while":[165],"having":[166],"5":[167],"30":[169],"times":[170],"fewer":[171],"parameters":[172],"than":[173],"architectures.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":12}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
