{"id":"https://openalex.org/W4288067780","doi":"https://doi.org/10.1145/3551647","title":"LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans","display_name":"LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans","publication_year":2022,"publication_date":"2022-07-27","ids":{"openalex":"https://openalex.org/W4288067780","doi":"https://doi.org/10.1145/3551647"},"language":"en","primary_location":{"id":"doi:10.1145/3551647","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3551647","pdf_url":null,"source":{"id":"https://openalex.org/S4210170305","display_name":"ACM Transactions on Management Information Systems","issn_l":"2158-656X","issn":["2158-656X","2158-6578"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Management Information Systems","raw_type":"journal-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/A5050935526","display_name":"Amogh Manoj Joshi","orcid":"https://orcid.org/0000-0002-6747-3780"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Amogh Manoj Joshi","raw_affiliation_strings":["Vivekanand Education Society\u2019s Institute of Technology, Mumbai, India","Vivekanand Education Society's Institute of Technology, Mumbai, India"],"raw_orcid":"https://orcid.org/0000-0002-6747-3780","affiliations":[{"raw_affiliation_string":"Vivekanand Education Society\u2019s Institute of Technology, Mumbai, India","institution_ids":[]},{"raw_affiliation_string":"Vivekanand Education Society's Institute of Technology, Mumbai, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046512961","display_name":"Deepak Ranjan Nayak","orcid":"https://orcid.org/0000-0002-8929-5778"},"institutions":[{"id":"https://openalex.org/I83205935","display_name":"Malaviya National Institute of Technology Jaipur","ror":"https://ror.org/0077k1j32","country_code":"IN","type":"education","lineage":["https://openalex.org/I83205935"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Deepak Ranjan Nayak","raw_affiliation_strings":["Malaviya National Institute of Technology, Jaipur, India"],"raw_orcid":"https://orcid.org/0000-0002-8929-5778","affiliations":[{"raw_affiliation_string":"Malaviya National Institute of Technology, Jaipur, India","institution_ids":["https://openalex.org/I83205935"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007222088","display_name":"Dibyasundar Das","orcid":"https://orcid.org/0000-0002-0285-2560"},"institutions":[{"id":"https://openalex.org/I16292982","display_name":"National Institute of Technology Rourkela","ror":"https://ror.org/011gmn932","country_code":"IN","type":"education","lineage":["https://openalex.org/I16292982"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dibyasundar Das","raw_affiliation_strings":["National Institute of Technology, Rourkela, India"],"raw_orcid":"https://orcid.org/0000-0002-0285-2560","affiliations":[{"raw_affiliation_string":"National Institute of Technology, Rourkela, India","institution_ids":["https://openalex.org/I16292982"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100434437","display_name":"Yudong Zhang","orcid":"https://orcid.org/0000-0002-4870-1493"},"institutions":[{"id":"https://openalex.org/I153648349","display_name":"University of Leicester","ror":"https://ror.org/04h699437","country_code":"GB","type":"education","lineage":["https://openalex.org/I153648349"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yudong Zhang","raw_affiliation_strings":["University of Leicester, Leicester, UK"],"raw_orcid":"https://orcid.org/0000-0002-4870-1493","affiliations":[{"raw_affiliation_string":"University of Leicester, Leicester, UK","institution_ids":["https://openalex.org/I153648349"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5050935526"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.4544,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.88965542,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"14","issue":"1","first_page":"1","last_page":"17"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":1.0,"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":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9828000068664551,"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"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9692000150680542,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.8870638608932495},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7825407981872559},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7753880023956299},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6521369218826294},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5727808475494385},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.5101792812347412},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5097762942314148},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49820423126220703},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4949278235435486},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.46800723671913147},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.41959941387176514},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3577612042427063},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3494410514831543},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.27815163135528564},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.16915521025657654},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09716978669166565}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8870638608932495},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7825407981872559},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7753880023956299},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6521369218826294},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5727808475494385},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.5101792812347412},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5097762942314148},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49820423126220703},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4949278235435486},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.46800723671913147},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.41959941387176514},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3577612042427063},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3494410514831543},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27815163135528564},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.16915521025657654},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09716978669166565},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3551647","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3551647","pdf_url":null,"source":{"id":"https://openalex.org/S4210170305","display_name":"ACM Transactions on Management Information Systems","issn_l":"2158-656X","issn":["2158-656X","2158-6578"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Management Information Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6200000047683716}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2097117768","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2253429366","https://openalex.org/W2531409750","https://openalex.org/W2612445135","https://openalex.org/W2765833400","https://openalex.org/W2962858109","https://openalex.org/W2963446712","https://openalex.org/W2970283086","https://openalex.org/W3004906315","https://openalex.org/W3005879071","https://openalex.org/W3007170347","https://openalex.org/W3008985036","https://openalex.org/W3014337038","https://openalex.org/W3015058572","https://openalex.org/W3017243633","https://openalex.org/W3019980738","https://openalex.org/W3022592783","https://openalex.org/W3023402713","https://openalex.org/W3025948831","https://openalex.org/W3035189651","https://openalex.org/W3044390618","https://openalex.org/W3045644133","https://openalex.org/W3046795633","https://openalex.org/W3089290459","https://openalex.org/W3103635657","https://openalex.org/W3104810384","https://openalex.org/W3106027361","https://openalex.org/W3107948185","https://openalex.org/W3157140100","https://openalex.org/W4287703073","https://openalex.org/W4288113232","https://openalex.org/W4297775537","https://openalex.org/W6745378982","https://openalex.org/W6774475223","https://openalex.org/W6775859349","https://openalex.org/W7042912420"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W2922073769","https://openalex.org/W3099765033","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Recent":[0],"years":[1],"have":[2],"witnessed":[3],"a":[4,31,66,139,195],"rise":[5],"in":[6,41,63,82,91,165],"employing":[7],"deep":[8],"learning":[9,79],"methods,":[10],"especially":[11],"convolutional":[12],"neural":[13],"networks":[14],"(CNNs)":[15],"for":[16,184],"detection":[17,57,185],"of":[18,26,34,44,86,168,177,182,186,203],"COVID-19":[19,142,187],"cases":[20],"using":[21,138],"chest":[22,50],"CT":[23,51,143,170,189,198],"scans.":[24,190],"Most":[25],"the":[27,42,56,98,146,150,166,192,201,204],"state-of-the-art":[28,162],"models":[29,160],"demand":[30],"huge":[32],"amount":[33],"parameters":[35,117],"which":[36],"often":[37],"suffer":[38],"from":[39,97,188],"overfitting":[40],"presence":[43,167],"limited":[45,169],"training":[46],"samples":[47],"such":[48],"as":[49],"data":[52],"and":[53,101,118,124,145,161,179],"thereby,":[54],"reducing":[55],"performance.":[58],"To":[59],"handle":[60],"these":[61],"issues,":[62],"this":[64],"paper,":[65],"lightweight":[67],"multi-scale":[68,95],"CNN":[69,131,159],"called":[70],"LiMS-Net":[71,75],"is":[72,105],"proposed.":[73],"The":[74,112],"contains":[76],"two":[77],"feature":[78],"blocks":[80],"where,":[81],"each":[83],"block,":[84],"filters":[85],"different":[87],"sizes":[88],"are":[89,135],"applied":[90],"parallel":[92],"to":[93,108,129],"derive":[94],"features":[96],"suspicious":[99],"regions":[100],"an":[102,175,180],"additional":[103],"filter":[104],"subsequently":[106],"employed":[107],"capture":[109],"discriminant":[110],"features.":[111],"model":[113,152,173],"has":[114],"only":[115],"2.53M":[116],"therefore,":[119],"requires":[120],"low":[121],"computational":[122],"cost":[123],"memory":[125],"space":[126],"when":[127],"compared":[128],"pretrained":[130,158],"architectures.":[132],"Comprehensive":[133],"experiments":[134],"carried":[136],"out":[137],"publicly":[140],"available":[141],"dataset":[144,199],"results":[147,193],"demonstrate":[148],"that":[149],"proposed":[151,205],"achieves":[153,174],"higher":[154],"performance":[155],"than":[156],"many":[157],"methods":[163],"even":[164],"data.":[171],"Our":[172],"accuracy":[176],"92.11%":[178],"F1-score":[181],"92.59%":[183],"Further,":[191],"on":[194],"relatively":[196],"larger":[197],"indicate":[200],"effectiveness":[202],"model.":[206]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}