{"id":"https://openalex.org/W4396852004","doi":"https://doi.org/10.1145/3644713.3644759","title":"Tuberculosis Detection Using Chest X-Ray Image Classification by Deep Learning","display_name":"Tuberculosis Detection Using Chest X-Ray Image Classification by Deep Learning","publication_year":2023,"publication_date":"2023-12-21","ids":{"openalex":"https://openalex.org/W4396852004","doi":"https://doi.org/10.1145/3644713.3644759"},"language":"en","primary_location":{"id":"doi:10.1145/3644713.3644759","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3644713.3644759","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Future Networks and Distributed Systems","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/A5089783578","display_name":"Romaissa Kebache","orcid":"https://orcid.org/0000-0002-0559-4296"},"institutions":[{"id":"https://openalex.org/I4210154551","display_name":"Centre Hospitalo-Universitaire Bab El Oued","ror":"https://ror.org/05qf3bt68","country_code":"DZ","type":"healthcare","lineage":["https://openalex.org/I4210154551"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Romaissa Kebache","raw_affiliation_strings":["LIAP Laboratory, University of El Oued, Algeria"],"raw_orcid":"https://orcid.org/0000-0002-0559-4296","affiliations":[{"raw_affiliation_string":"LIAP Laboratory, University of El Oued, Algeria","institution_ids":["https://openalex.org/I4210154551"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006883497","display_name":"Abdelkader Laouid","orcid":"https://orcid.org/0000-0002-8175-8467"},"institutions":[{"id":"https://openalex.org/I4210154551","display_name":"Centre Hospitalo-Universitaire Bab El Oued","ror":"https://ror.org/05qf3bt68","country_code":"DZ","type":"healthcare","lineage":["https://openalex.org/I4210154551"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Abdelkader Laouid","raw_affiliation_strings":["LIAP Laboratory, University of El Oued, Algeria"],"raw_orcid":"https://orcid.org/0000-0002-8175-8467","affiliations":[{"raw_affiliation_string":"LIAP Laboratory, University of El Oued, Algeria","institution_ids":["https://openalex.org/I4210154551"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032218268","display_name":"Sana Sahar Guia","orcid":"https://orcid.org/0000-0001-9101-5491"},"institutions":[{"id":"https://openalex.org/I4210154551","display_name":"Centre Hospitalo-Universitaire Bab El Oued","ror":"https://ror.org/05qf3bt68","country_code":"DZ","type":"healthcare","lineage":["https://openalex.org/I4210154551"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Sana Sahar Guia","raw_affiliation_strings":["LIAP Laboratory, University of El Oued, Algeria"],"raw_orcid":"https://orcid.org/0000-0001-9101-5491","affiliations":[{"raw_affiliation_string":"LIAP Laboratory, University of El Oued, Algeria","institution_ids":["https://openalex.org/I4210154551"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042385111","display_name":"Mostefa Kara","orcid":"https://orcid.org/0000-0002-5736-8039"},"institutions":[{"id":"https://openalex.org/I4210154551","display_name":"Centre Hospitalo-Universitaire Bab El Oued","ror":"https://ror.org/05qf3bt68","country_code":"DZ","type":"healthcare","lineage":["https://openalex.org/I4210154551"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Mostefa Kara","raw_affiliation_strings":["LIAP Laboratory, University of El Oued, Algeria"],"raw_orcid":"https://orcid.org/0000-0002-5736-8039","affiliations":[{"raw_affiliation_string":"LIAP Laboratory, University of El Oued, Algeria","institution_ids":["https://openalex.org/I4210154551"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079210801","display_name":"Nassima Bouadem","orcid":"https://orcid.org/0000-0002-5572-6365"},"institutions":[{"id":"https://openalex.org/I187560010","display_name":"University of B\u00e9ja\u00efa","ror":"https://ror.org/03yb2hp88","country_code":"DZ","type":"education","lineage":["https://openalex.org/I187560010"]}],"countries":["DZ"],"is_corresponding":false,"raw_author_name":"Nassima Bouadem","raw_affiliation_strings":["LIMED Laboratory, University of Bejaia, Algeria"],"raw_orcid":"https://orcid.org/0000-0002-5572-6365","affiliations":[{"raw_affiliation_string":"LIMED Laboratory, University of Bejaia, Algeria","institution_ids":["https://openalex.org/I187560010"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7506,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.85691643,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"352","last_page":"356"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9980999827384949,"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":0.9980999827384949,"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":0.996399998664856,"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/T10862","display_name":"AI in cancer detection","score":0.9783999919891357,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6179112195968628},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5953629612922668},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5135836601257324},{"id":"https://openalex.org/keywords/tuberculosis","display_name":"Tuberculosis","score":0.4953542947769165},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4752923250198364},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4288427233695984},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4092147946357727},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3337237238883972},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.331514835357666},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2932248115539551},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.12850961089134216}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6179112195968628},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5953629612922668},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5135836601257324},{"id":"https://openalex.org/C2781069245","wikidata":"https://www.wikidata.org/wiki/Q12204","display_name":"Tuberculosis","level":2,"score":0.4953542947769165},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4752923250198364},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4288427233695984},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4092147946357727},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3337237238883972},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.331514835357666},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2932248115539551},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.12850961089134216}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3644713.3644759","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3644713.3644759","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Future Networks and Distributed Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2774788879","https://openalex.org/W2806699389","https://openalex.org/W2884585870","https://openalex.org/W2964322910","https://openalex.org/W2985778816","https://openalex.org/W3031210037","https://openalex.org/W3045801508","https://openalex.org/W3088086017","https://openalex.org/W3092048782","https://openalex.org/W3099183222","https://openalex.org/W4205283519","https://openalex.org/W4206610718","https://openalex.org/W4210390897","https://openalex.org/W4220665606","https://openalex.org/W4226538140"],"related_works":["https://openalex.org/W2806061655","https://openalex.org/W1955737261","https://openalex.org/W2775573077","https://openalex.org/W2765354416","https://openalex.org/W2110949356","https://openalex.org/W4375867731","https://openalex.org/W2796117997","https://openalex.org/W2795259429","https://openalex.org/W2291489469","https://openalex.org/W2546503577"],"abstract_inverted_index":{"Tuberculosis":[0],"(TB)":[1],"is":[2,10,30,62],"a":[3,55,72],"deadly":[4],"and":[5,85,123,137,142],"widespread":[6],"lung":[7],"disease":[8],"that":[9],"often":[11],"not":[12],"easily":[13],"detectable":[14],"in":[15,48,91,130],"the":[16,21,36,49,88,94],"early":[17],"stages.":[18],"Thanks":[19],"to":[20,33,93,98,107],"availability":[22],"of":[23,39,74,135],"high-resolution":[24],"chest":[25,65],"X-rays,":[26],"deep":[27,78],"learning":[28,79],"(DL)":[29],"now":[31],"able":[32],"help":[34],"with":[35,44],"successful":[37],"detection":[38,61],"this":[40,53],"malignant":[41],"disease,":[42],"along":[43],"other":[45],"possible":[46],"applications":[47],"health":[50],"sector.":[51],"In":[52],"manuscript,":[54],"new":[56],"deep-learning":[57],"model":[58],"for":[59,139],"TB":[60,121],"proposed":[63],"using":[64],"X-ray":[66],"image":[67],"classification.":[68],"To":[69],"achieve":[70],"this,":[71],"mixture":[73],"two":[75],"popular":[76,114],"pre-trained":[77],"CNNs":[80],"has":[81,104],"been":[82,105],"employed":[83],"(VGG16":[84],"VGG19)":[86],"utilizing":[87],"ImageNet":[89],"dataset,":[90,117,119,122],"addition":[92],"block":[95],"attention":[96],"module":[97],"obtain":[99],"spatial":[100],"data.":[101],"This":[102],"method":[103],"proven":[106],"be":[108],"valid":[109],"through":[110],"experiments":[111],"on":[112],"four":[113],"Datasets;":[115],"NLM":[116],"Belarus":[118],"NIAID":[120],"RSNA-CXR":[124],"dataset.":[125],"The":[126],"evaluation":[127],"showed":[128],"results":[129],"achieving":[131],"an":[132],"excellent":[133],"accuracy":[134],"0.9966":[136],"0.9978":[138],"both":[140],"training":[141],"validation":[143],"sets":[144],"respectively.":[145]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7}],"updated_date":"2026-06-13T07:54:00.901334","created_date":"2025-10-10T00:00:00"}
