{"id":"https://openalex.org/W4394898298","doi":"https://doi.org/10.1145/3650400.3650632","title":"YOLO lung CT disease rapid detection classification with fused attention mechanism","display_name":"YOLO lung CT disease rapid detection classification with fused attention mechanism","publication_year":2023,"publication_date":"2023-10-20","ids":{"openalex":"https://openalex.org/W4394898298","doi":"https://doi.org/10.1145/3650400.3650632"},"language":"en","primary_location":{"id":"doi:10.1145/3650400.3650632","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3650400.3650632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering","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":null,"display_name":"Qinghua Su","orcid":"https://orcid.org/0009-0002-9927-7503"},"institutions":[{"id":"https://openalex.org/I176432857","display_name":"Beijing Wuzi University","ror":"https://ror.org/00bd1d647","country_code":"CN","type":"education","lineage":["https://openalex.org/I176432857"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinghua Su","raw_affiliation_strings":["College of Information, Beijing WUZI University, China"],"raw_orcid":"https://orcid.org/0009-0002-9927-7503","affiliations":[{"raw_affiliation_string":"College of Information, Beijing WUZI University, China","institution_ids":["https://openalex.org/I176432857"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102806083","display_name":"Zhenbo Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I176432857","display_name":"Beijing Wuzi University","ror":"https://ror.org/00bd1d647","country_code":"CN","type":"education","lineage":["https://openalex.org/I176432857"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenbo Qin","raw_affiliation_strings":["College of Information, Beijing WUZI University, China"],"raw_orcid":"https://orcid.org/0009-0004-0284-5303","affiliations":[{"raw_affiliation_string":"College of Information, Beijing WUZI University, China","institution_ids":["https://openalex.org/I176432857"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5095748265","display_name":"Jianhong Mu","orcid":"https://orcid.org/0009-0003-0121-1571"},"institutions":[{"id":"https://openalex.org/I176432857","display_name":"Beijing Wuzi University","ror":"https://ror.org/00bd1d647","country_code":"CN","type":"education","lineage":["https://openalex.org/I176432857"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhong Mu","raw_affiliation_strings":["College of Information, Beijing WUZI University, China"],"raw_orcid":"https://orcid.org/0009-0003-0121-1571","affiliations":[{"raw_affiliation_string":"College of Information, Beijing WUZI University, China","institution_ids":["https://openalex.org/I176432857"]}]},{"author_position":"last","author":{"id":null,"display_name":"Hao Wu","orcid":"https://orcid.org/0009-0009-1323-9321"},"institutions":[{"id":"https://openalex.org/I176432857","display_name":"Beijing Wuzi University","ror":"https://ror.org/00bd1d647","country_code":"CN","type":"education","lineage":["https://openalex.org/I176432857"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Wu","raw_affiliation_strings":["College of Information, Beijing WUZI University, China"],"raw_orcid":"https://orcid.org/0009-0009-1323-9321","affiliations":[{"raw_affiliation_string":"College of Information, Beijing WUZI University, China","institution_ids":["https://openalex.org/I176432857"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I176432857"],"apc_list":null,"apc_paid":null,"fwci":0.2163,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.62819432,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1381","last_page":"1387"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9854000210762024,"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":0.9588000178337097,"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/computer-science","display_name":"Computer science","score":0.7209877371788025},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7056162357330322},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6056666970252991},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6050444841384888},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5520685315132141},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5454140901565552},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.5216858386993408},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.46994689106941223},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.46012163162231445},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4003397226333618}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7209877371788025},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7056162357330322},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6056666970252991},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6050444841384888},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5520685315132141},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5454140901565552},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5216858386993408},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.46994689106941223},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.46012163162231445},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4003397226333618},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3650400.3650632","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3650400.3650632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering","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":14,"referenced_works":["https://openalex.org/W2044097773","https://openalex.org/W2123045220","https://openalex.org/W2295107390","https://openalex.org/W3010182902","https://openalex.org/W3034635466","https://openalex.org/W3095629061","https://openalex.org/W3133954504","https://openalex.org/W3184886727","https://openalex.org/W4214860167","https://openalex.org/W4234552385","https://openalex.org/W4238524038","https://openalex.org/W4402601233","https://openalex.org/W6743731764","https://openalex.org/W6753412334"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4239306820","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2969228573","https://openalex.org/W2963690996","https://openalex.org/W2072289174","https://openalex.org/W4382050342"],"abstract_inverted_index":{"Currently,":[0],"although":[1],"the":[2,15,60,80,102,106,117,150,154,159,176,181,185,205,211],"use":[3,126],"of":[4,86,93,116,129,145,195,201],"convolutional":[5],"neural":[6],"networks":[7],"(CNN)":[8],"for":[9,28,46,75,111,140],"detecting":[10],"lung":[11,47,84,108,146,227],"infection":[12],"has":[13,22],"improved":[14],"detection":[16],"performance":[17],"and":[18,55,78,90,113,136,143,148,167,197,219],"efficiency,":[19],"it":[20],"still":[21],"certain":[23],"shortcomings,":[24],"low":[25],"feature":[26],"utilization":[27],"images":[29,49,57,89,95,110],"or":[30],"difficulty":[31],"in":[32,217,221,230],"focusing":[33],"key":[34,165],"features.":[35],"An":[36],"effective":[37],"YOLO":[38,64,155,182,209,213],"algorithm":[39],"with":[40,65,120,184,191],"fused":[41,66,121,186,206],"attention":[42,67,122,151,187,207],"mechanism":[43,68,123,152,188,208],"is":[44,69],"proposed":[45],"CT":[48,88,94,109],"to":[50,58,161],"detect":[51],"normal,":[52],"common":[53],"pneumonia":[54],"COVID-19":[56],"address":[59],"above":[61],"problems.":[62],"The":[63,173],"mainly":[70],"divided":[71],"into":[72,153],"two":[73],"parts":[74],"model":[76,114,119,160,183,214],"training":[77,112],"experiments:":[79],"first":[81],"step":[82,104],"performs":[83],"segmentation":[85,139,142],"chest":[87],"data":[91],"cleaning":[92],"based":[96],"on":[97,164,175],"physician":[98],"diagnostic":[99],"image":[100],"values;":[101],"second":[103],"uses":[105],"cleaned":[107],"evaluation":[115],"Yolov5":[118],"(CBAM).":[124],"We":[125],"a":[127,198,231],"series":[128],"operations":[130],"such":[131],"as":[132],"binarization,":[133],"expansion":[134],"erosion":[135],"connected":[137],"domain":[138],"initial":[141],"filtering":[144],"images,":[147],"incorporate":[149],"model,":[156],"which":[157,223],"enables":[158],"better":[162],"focus":[163],"features":[166],"avoid":[168],"interference":[169],"from":[170],"erroneous":[171],"data.":[172],"results":[174],"COVID-19x":[177],"dataset":[178],"show":[179],"that":[180],"detects":[189],"classification":[190],"an":[192],"accuracy":[193,218],"rate":[194,200],"0.85":[196],"recall":[199],"0.88.":[202],"In":[203],"summary,":[204],"outperforms":[210],"original":[212],"by":[215],"6.5%":[216],"8.8%":[220],"recall,":[222],"helps":[224],"clinicians":[225],"diagnose":[226],"inflammatory":[228],"infections":[229],"timely":[232],"manner":[233],"type.":[234]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}