{"id":"https://openalex.org/W4292347793","doi":"https://doi.org/10.1109/jbhi.2022.3196489","title":"MFL-Net: An Efficient Lightweight Multi-Scale Feature Learning CNN for COVID-19 Diagnosis From CT Images","display_name":"MFL-Net: An Efficient Lightweight Multi-Scale Feature Learning CNN for COVID-19 Diagnosis From CT Images","publication_year":2022,"publication_date":"2022-08-19","ids":{"openalex":"https://openalex.org/W4292347793","doi":"https://doi.org/10.1109/jbhi.2022.3196489","pmid":"https://pubmed.ncbi.nlm.nih.gov/35981061"},"language":"en","primary_location":{"id":"doi:10.1109/jbhi.2022.3196489","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2022.3196489","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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":false,"raw_author_name":"Amogh Manoj Joshi","raw_affiliation_strings":["Department of Electronics and Telecommunication Engineering, Vivekanand Education Society's Institute of Technology, Mumbai, Maharashtra, India"],"raw_orcid":"https://orcid.org/0000-0002-6747-3780","affiliations":[{"raw_affiliation_string":"Department of Electronics and Telecommunication Engineering, Vivekanand Education Society's Institute of Technology, Mumbai, Maharashtra, India","institution_ids":[]}]},{"author_position":"last","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":["Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India"],"raw_orcid":"https://orcid.org/0000-0002-8929-5778","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India","institution_ids":["https://openalex.org/I83205935"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.3115,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.95025052,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"26","issue":"11","first_page":"5355","last_page":"5363"},"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":0.9850000143051147,"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":0.9681000113487244,"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":0.85761958360672},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8332846164703369},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.816728949546814},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6551231145858765},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5373754501342773},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5225493311882019},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5224663019180298},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4943206310272217},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4929230213165283},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4511125683784485},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.342673122882843},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.27909567952156067}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.85761958360672},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8332846164703369},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.816728949546814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6551231145858765},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5373754501342773},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5225493311882019},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5224663019180298},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4943206310272217},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4929230213165283},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4511125683784485},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.342673122882843},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27909567952156067},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000086382","descriptor_name":"COVID-19","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D000086382","descriptor_name":"COVID-19","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D000086382","descriptor_name":"COVID-19","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D000086742","descriptor_name":"COVID-19 Testing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000086742","descriptor_name":"COVID-19 Testing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000086742","descriptor_name":"COVID-19 Testing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/jbhi.2022.3196489","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2022.3196489","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},{"id":"pmid:35981061","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35981061","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE journal of biomedical and health informatics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1533861849","https://openalex.org/W1677182931","https://openalex.org/W2097117768","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2253429366","https://openalex.org/W2417429787","https://openalex.org/W2531409750","https://openalex.org/W2765833400","https://openalex.org/W2794022343","https://openalex.org/W2901030517","https://openalex.org/W2955425717","https://openalex.org/W2962858109","https://openalex.org/W2963163009","https://openalex.org/W2963446712","https://openalex.org/W2979854708","https://openalex.org/W3006643024","https://openalex.org/W3007170347","https://openalex.org/W3008985036","https://openalex.org/W3014725478","https://openalex.org/W3015058572","https://openalex.org/W3015596934","https://openalex.org/W3016513502","https://openalex.org/W3017243633","https://openalex.org/W3019980738","https://openalex.org/W3022592783","https://openalex.org/W3023180050","https://openalex.org/W3025948831","https://openalex.org/W3033272814","https://openalex.org/W3044390618","https://openalex.org/W3044680687","https://openalex.org/W3049243001","https://openalex.org/W3085306326","https://openalex.org/W3089290459","https://openalex.org/W3103635657","https://openalex.org/W3104810384","https://openalex.org/W3106027361","https://openalex.org/W3126674636","https://openalex.org/W3128131898","https://openalex.org/W3133191822","https://openalex.org/W3134583934","https://openalex.org/W3142371777","https://openalex.org/W3145352525","https://openalex.org/W3151077464","https://openalex.org/W3155958087","https://openalex.org/W3157140100","https://openalex.org/W3161864725","https://openalex.org/W3163405749","https://openalex.org/W3175988796","https://openalex.org/W3176429669","https://openalex.org/W3187766292","https://openalex.org/W3200012255","https://openalex.org/W4245639997","https://openalex.org/W4280584058","https://openalex.org/W6631943919","https://openalex.org/W6745378982","https://openalex.org/W6762718338","https://openalex.org/W6781435299","https://openalex.org/W6781659976"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W4297676672","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":{"Timely":[0],"and":[1,24,42,143,154,166,171,208,240],"accurate":[2,239],"diagnosis":[3,242],"of":[4,18,27,66,73,83,98,131,243],"coronavirus":[5],"disease":[6],"2019":[7],"(COVID-19)":[8],"is":[9],"crucial":[10],"in":[11,80,237],"curbing":[12],"its":[13],"spread.":[14],"Slow":[15],"testing":[16],"results":[17,195],"reverse":[19],"transcription-polymerase":[20],"chain":[21],"reaction":[22],"(RT-PCR)":[23],"a":[25,70,129],"shortage":[26],"test":[28],"kits":[29],"have":[30,54],"led":[31],"to":[32,57,108,175,228],"consider":[33],"chest":[34,62],"computed":[35],"tomography":[36],"(CT)":[37],"as":[38,124],"an":[39,114,220,230],"alternative":[40],"screening":[41],"diagnostic":[43],"tool.":[44],"Many":[45],"deep":[46],"learning":[47,121],"methods,":[48],"especially":[49],"convolutional":[50,137],"neural":[51],"networks":[52],"(CNNs),":[53],"been":[55],"developed":[56],"detect":[58],"COVID-19":[59,190],"cases":[60],"from":[61,78],"CT":[63,191],"scans.":[64],"Most":[65],"these":[67,110],"models":[68,94,207],"demand":[69],"vast":[71],"number":[72],"parameters":[74,165],"which":[75],"often":[76],"suffer":[77],"overfitting":[79],"the":[81,88,96,102,158,198,234,238],"presence":[82],"limited":[84,215],"training":[85,216],"data.":[86],"Moreover,":[87],"linearly":[89],"stacked":[90],"single-branched":[91],"architecture":[92],"based":[93],"hamper":[95],"extraction":[97],"multi-scale":[99,119,149],"features,":[100],"reducing":[101],"detection":[103],"performance.":[104],"In":[105],"this":[106],"paper,":[107],"handle":[109],"issues,":[111],"we":[112],"propose":[113],"extremely":[115,221],"lightweight":[116,222],"CNN":[117,179,206],"with":[118,139,214],"feature":[120],"blocks":[122,133],"called":[123],"MFL-Net.":[125],"The":[126,160,194,224],"MFL-Net":[127],"comprises":[128],"sequence":[130],"MFL":[132],"that":[134,197],"combines":[135],"multiple":[136],"layers":[138],"3":[140],"\u00d73":[141],"filters":[142],"residual":[144],"connections":[145],"effectively,":[146],"thereby":[147],"extracting":[148],"features":[150],"at":[151],"different":[152],"levels":[153],"preserving":[155],"them":[156],"throughout":[157],"block.":[159],"model":[161,200],"has":[162],"only":[163],"0.78M":[164],"requires":[167],"low":[168],"computational":[169],"cost":[170],"memory":[172],"space":[173],"compared":[174],"many":[176],"ImageNet":[177],"pretrained":[178,205],"architectures.":[180],"Comprehensive":[181],"experiments":[182],"are":[183],"carried":[184],"out":[185],"using":[186],"two":[187],"publicly":[188],"available":[189],"imaging":[192],"datasets.":[193],"demonstrate":[196],"proposed":[199,225],"achieves":[201],"higher":[202],"performance":[203],"than":[204],"state-of-the-art":[209],"methods":[210],"on":[211],"both":[212],"datasets":[213],"data":[217],"despite":[218],"having":[219],"architecture.":[223],"method":[226],"proves":[227],"be":[229],"effective":[231],"aid":[232],"for":[233],"healthcare":[235],"system":[236],"timely":[241],"COVID-19.":[244]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}