{"id":"https://openalex.org/W4312539135","doi":"https://doi.org/10.1109/ijcnn55064.2022.9891950","title":"GDenseMNet: Global Dense Multiscale Feature Learning Network for Efficient COVID-19 Detection in CT Images","display_name":"GDenseMNet: Global Dense Multiscale Feature Learning Network for Efficient COVID-19 Detection in CT Images","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312539135","doi":"https://doi.org/10.1109/ijcnn55064.2022.9891950"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9891950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9891950","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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 Joint Conference on Neural Networks (IJCNN)","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/A5050935526","display_name":"Amogh Manoj Joshi","orcid":"https://orcid.org/0000-0002-6747-3780"},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]},{"id":"https://openalex.org/I169877490","display_name":"University of Mumbai","ror":"https://ror.org/032hdk172","country_code":"IN","type":"education","lineage":["https://openalex.org/I169877490"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Amogh Manoj Joshi","raw_affiliation_strings":["V. E. S. Institute of Technology,Dept. of Electronics and Telecom Engineering,Mumbai,India","Dept. of Electronics and Telecom Engineering, V. E. S. Institute of Technology, Mumbai, India"],"affiliations":[{"raw_affiliation_string":"V. E. S. Institute of Technology,Dept. of Electronics and Telecom Engineering,Mumbai,India","institution_ids":["https://openalex.org/I169877490"]},{"raw_affiliation_string":"Dept. of Electronics and Telecom Engineering, V. E. S. Institute of Technology, Mumbai, India","institution_ids":["https://openalex.org/I162827531"]}]},{"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":["Malaviya National Institute of Technology Jaipur,Dept. of Computer Science and Engineering,Jaipur,India","Dept. of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, India"],"affiliations":[{"raw_affiliation_string":"Malaviya National Institute of Technology Jaipur,Dept. of Computer Science and Engineering,Jaipur,India","institution_ids":["https://openalex.org/I83205935"]},{"raw_affiliation_string":"Dept. of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, India","institution_ids":["https://openalex.org/I83205935"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5050935526"],"corresponding_institution_ids":["https://openalex.org/I162827531","https://openalex.org/I169877490"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25280647,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9843000173568726,"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":0.9842000007629395,"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/computer-science","display_name":"Computer science","score":0.7961126565933228},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7127280235290527},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7117235064506531},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.701386570930481},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6631338000297546},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6533353924751282},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.584450900554657},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5537784099578857},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5415814518928528},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4959960877895355},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.43166443705558777},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3674923777580261},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3315131664276123},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.13416054844856262}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7961126565933228},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7127280235290527},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7117235064506531},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.701386570930481},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6631338000297546},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6533353924751282},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.584450900554657},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5537784099578857},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5415814518928528},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4959960877895355},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.43166443705558777},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3674923777580261},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3315131664276123},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.13416054844856262},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9891950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9891950","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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 Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.49000000953674316,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W2097117768","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2253429366","https://openalex.org/W2417429787","https://openalex.org/W2963446712","https://openalex.org/W2979854708","https://openalex.org/W3007170347","https://openalex.org/W3015058572","https://openalex.org/W3017243633","https://openalex.org/W3022592783","https://openalex.org/W3044390618","https://openalex.org/W3044680687","https://openalex.org/W3085306326","https://openalex.org/W3086462707","https://openalex.org/W3104810384","https://openalex.org/W3106027361","https://openalex.org/W3133191822","https://openalex.org/W3151077464","https://openalex.org/W3155958087","https://openalex.org/W3157140100","https://openalex.org/W3175988796","https://openalex.org/W3200012255","https://openalex.org/W4297775537","https://openalex.org/W6725739302","https://openalex.org/W6737664043","https://openalex.org/W6776522219","https://openalex.org/W6776523511","https://openalex.org/W6781435299","https://openalex.org/W6781539115","https://openalex.org/W6785726762","https://openalex.org/W7042912420"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4295815739","https://openalex.org/W2915512385","https://openalex.org/W4309346246"],"abstract_inverted_index":{"Accurate":[0],"and":[1,68,74,82,91,101,113,174,198,221],"rapid":[2],"diagnosis":[3],"of":[4,59,166],"COVID-19":[5,37,49,137,232],"is":[6,194],"crucial":[7],"for":[8,35,48,136],"curbing":[9],"its":[10],"fast":[11],"spread":[12],"across":[13],"the":[14,36,201,209],"globe,":[15],"with":[16],"constant":[17],"mutations":[18],"leading":[19],"to":[20,87,96,217],"newer":[21],"variants.":[22],"Recent":[23],"studies":[24],"have":[25,44,84],"exhibited":[26],"that":[27,142,162,208],"chest":[28],"CT":[29,140],"scans":[30],"manifest":[31],"clear":[32],"radiological":[33],"findings":[34],"infected":[38],"patients.":[39],"Convolutional":[40],"neural":[41,77],"networks":[42,78,108],"(CNN)":[43],"been":[45,85],"used":[46],"considerably":[47],"diagnosis;":[50],"however,":[51],"most":[52],"CNN":[53,126,219],"architectures":[54],"demand":[55],"a":[56,69,124],"huge":[57],"amount":[58],"parameters,":[60],"resulting":[61],"in":[62,111],"overfitting":[63],"on":[64],"limited":[65],"training":[66],"data":[67],"slower":[70],"inference.":[71],"Further,":[72],"residual":[73,175],"densely":[75],"connected":[76],"such":[79],"as":[80,227],"ResNet":[81],"DenseNet":[83],"proven":[86],"strengthen":[88],"feature":[89,92,132,158,188],"extraction":[90,159],"propagation":[93],"but":[94],"fail":[95],"fully":[97],"discover":[98],"both":[99],"local":[100,157,164],"global":[102,129,145,179,187],"representations.":[103],"Moreover,":[104],"few":[105],"linearly":[106],"stacked":[107],"fall":[109],"short":[110],"capturing":[112,149],"preserving":[114],"multiscaled":[115,150],"features":[116,165],"from":[117,139],"various":[118,167],"receptive":[119,169],"fields.":[120],"This":[121],"paper":[122],"proposes":[123],"new":[125],"architecture":[127,193],"called":[128],"dense":[130,146,180],"multiscale":[131,156],"learning":[133,189],"network":[134],"(GDenseMNet)":[135],"detection":[138,214],"images":[141],"effectively":[143],"incorporates":[144],"connections":[147,181],"while":[148],"features.":[151],"The":[152,178,191],"GDenseMNet":[153,210],"model":[154,211],"comprises":[155],"(MLF)":[160],"blocks":[161,184],"capture":[163],"size":[168],"fields":[170],"using":[171,200],"multiple":[172],"filters":[173],"skip":[176],"connections.":[177],"between":[182],"these":[183],"further":[185],"enable":[186],"capability.":[190],"proposed":[192],"lightweight,":[195],"end-to-end":[196],"learnable,":[197],"validated":[199],"SARS-CoV-2":[202],"CT-Scan":[203],"dataset.":[204],"Experimental":[205],"results":[206],"demonstrate":[207],"achieves":[212],"promising":[213],"performance":[215],"compared":[216],"state-of-the-art":[218],"approaches":[220],"hence,":[222],"it":[223],"can":[224],"be":[225],"utilized":[226],"an":[228],"effective":[229],"tool":[230],"real-time":[231],"diagnosis.":[233]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}