{"id":"https://openalex.org/W4405449714","doi":"https://doi.org/10.3389/fdata.2024.1489020","title":"Leveraging compact convolutional transformers for enhanced COVID-19 detection in chest X-rays: a grad-CAM visualization approach","display_name":"Leveraging compact convolutional transformers for enhanced COVID-19 detection in chest X-rays: a grad-CAM visualization approach","publication_year":2024,"publication_date":"2024-12-16","ids":{"openalex":"https://openalex.org/W4405449714","doi":"https://doi.org/10.3389/fdata.2024.1489020","pmid":"https://pubmed.ncbi.nlm.nih.gov/39736985"},"language":"en","primary_location":{"id":"doi:10.3389/fdata.2024.1489020","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2024.1489020","pdf_url":"https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1489020/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1489020/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110642816","display_name":"Aravinda C. V","orcid":null},"institutions":[{"id":"https://openalex.org/I1333540553","display_name":"Nitte University","ror":"https://ror.org/029nydt37","country_code":"IN","type":"education","lineage":["https://openalex.org/I1333540553"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Aravinda C. V","raw_affiliation_strings":["Department of Computer Science and Engineering, NITTE Mahalinga Adyantaya Memorial Institute of Technology, NITTE Deemed to Be University, Karkala, Karnataka, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, NITTE Mahalinga Adyantaya Memorial Institute of Technology, NITTE Deemed to Be University, Karkala, Karnataka, India","institution_ids":["https://openalex.org/I1333540553"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102480356","display_name":"Singapogu Ravikiran B.","orcid":"https://orcid.org/0000-0003-1363-3598"},"institutions":[{"id":"https://openalex.org/I1333540553","display_name":"Nitte University","ror":"https://ror.org/029nydt37","country_code":"IN","type":"education","lineage":["https://openalex.org/I1333540553"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Sudeepa K. B","raw_affiliation_strings":["Department of Computer Science and Engineering, NITTE Mahalinga Adyantaya Memorial Institute of Technology, NITTE Deemed to Be University, Karkala, Karnataka, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, NITTE Mahalinga Adyantaya Memorial Institute of Technology, NITTE Deemed to Be University, Karkala, Karnataka, India","institution_ids":["https://openalex.org/I1333540553"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025225158","display_name":"Sushma Pradeep","orcid":"https://orcid.org/0000-0002-9403-234X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S. Pradeep","raw_affiliation_strings":["Department of Computer Science and Engineering, Government Engineering College, Chamarajanagar, Karnataka, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Government Engineering College, Chamarajanagar, Karnataka, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115494653","display_name":"P. Suraksha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"P. Suraksha","raw_affiliation_strings":["Department of Computer Science and Engineering, Vidhya Vardhaka College of Engineering, Mysore, Karnataka, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Vidhya Vardhaka College of Engineering, Mysore, Karnataka, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076579498","display_name":"Lin Meng","orcid":"https://orcid.org/0000-0003-4351-6923"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Meng Lin","raw_affiliation_strings":["Department of Electronic and Computer Engineering (The Graduate School of Science and Engineering), Ritsumeikan University, Kusatsu, Shiga, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic and Computer Engineering (The Graduate School of Science and Engineering), Ritsumeikan University, Kusatsu, Shiga, Japan","institution_ids":["https://openalex.org/I135768898"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102480356","https://openalex.org/A5110642816"],"corresponding_institution_ids":["https://openalex.org/I1333540553"],"apc_list":{"value":1150,"currency":"USD","value_usd":1150},"apc_paid":{"value":1150,"currency":"USD","value_usd":1150},"fwci":0.7257,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.74989312,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"7","issue":null,"first_page":"1489020","last_page":"1489020"},"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":0.9818000197410583,"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":0.9771999716758728,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7416601181030273},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5818976163864136},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5314447283744812},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.5263287425041199},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.4923354387283325},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.4665515124797821},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4398721158504486},{"id":"https://openalex.org/keywords/radiography","display_name":"Radiography","score":0.4361693263053894},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.42634865641593933},{"id":"https://openalex.org/keywords/pneumonia","display_name":"Pneumonia","score":0.4179065525531769},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39096102118492126},{"id":"https://openalex.org/keywords/medical-physics","display_name":"Medical physics","score":0.36794137954711914},{"id":"https://openalex.org/keywords/intensive-care-medicine","display_name":"Intensive care medicine","score":0.3574588894844055},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.33892661333084106},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.22779813408851624},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.13016065955162048}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7416601181030273},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5818976163864136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5314447283744812},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5263287425041199},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.4923354387283325},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.4665515124797821},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4398721158504486},{"id":"https://openalex.org/C36454342","wikidata":"https://www.wikidata.org/wiki/Q245341","display_name":"Radiography","level":2,"score":0.4361693263053894},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.42634865641593933},{"id":"https://openalex.org/C2777914695","wikidata":"https://www.wikidata.org/wiki/Q12192","display_name":"Pneumonia","level":2,"score":0.4179065525531769},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39096102118492126},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.36794137954711914},{"id":"https://openalex.org/C177713679","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care medicine","level":1,"score":0.3574588894844055},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.33892661333084106},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.22779813408851624},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.13016065955162048},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3389/fdata.2024.1489020","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2024.1489020","pdf_url":"https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1489020/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},{"id":"pmid:39736985","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/39736985","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":"Frontiers in big data","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:11683681","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11683681","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11683681/pdf/fdata-07-1489020.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Front Big Data","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:19dac8a0e7c94d1294dae19dbaa4bd2c","is_oa":false,"landing_page_url":"https://doaj.org/article/19dac8a0e7c94d1294dae19dbaa4bd2c","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Frontiers in Big Data, Vol 7 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3389/fdata.2024.1489020","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2024.1489020","pdf_url":"https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1489020/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320331825","display_name":"NMAM Institute of Technology","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4405449714.pdf","grobid_xml":"https://content.openalex.org/works/W4405449714.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2194775991","https://openalex.org/W2611650229","https://openalex.org/W2788633781","https://openalex.org/W2949942603","https://openalex.org/W2951934944","https://openalex.org/W3006082171","https://openalex.org/W3006627382","https://openalex.org/W3007497549","https://openalex.org/W3011149445","https://openalex.org/W3013019084","https://openalex.org/W3013507463","https://openalex.org/W3013601031","https://openalex.org/W3014725478","https://openalex.org/W3017855299","https://openalex.org/W3023142498","https://openalex.org/W3025693254","https://openalex.org/W3027764902","https://openalex.org/W3028521397","https://openalex.org/W3048670851","https://openalex.org/W3048749423","https://openalex.org/W3101156210","https://openalex.org/W3104810384","https://openalex.org/W3105081694","https://openalex.org/W3135243128","https://openalex.org/W4281492658","https://openalex.org/W4285044121","https://openalex.org/W4285048387","https://openalex.org/W4285505903","https://openalex.org/W6637373629","https://openalex.org/W6775352234","https://openalex.org/W6777982376","https://openalex.org/W7015098694"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4391621807","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3024479225","https://openalex.org/W4323287533","https://openalex.org/W3171371563","https://openalex.org/W3003847115"],"abstract_inverted_index":{"The":[0,217,435,690,753,832,1054,1105,1281,1370,1561,1724,2209,2338,2380,2463,2506,2607,2704,2788,2940,2949,3170,3324,3372,3506,3589,3608,3685,3718,3736,3796,3887,3943,4024,4072,4547,4606,4813,4848,4909,5021,5241,5253,5344,5448,5473,5549,5719,5759,5876,5956,6077,6131,6165,6524,6586],"evolution":[1],"of":[2,12,19,157,170,174,182,210,219,304,310,362,430,438,446,458,533,598,629,686,692,700,704,749,755,844,888,952,969,978,994,1022,1033,1046,1056,1102,1107,1112,1145,1162,1205,1246,1308,1334,1347,1363,1393,1418,1421,1432,1462,1470,1484,1596,1626,1727,1746,1762,1771,1827,1861,1889,1896,1907,1946,1960,1970,1980,1986,2009,2029,2046,2062,2073,2117,2157,2181,2195,2233,2261,2285,2292,2302,2356,2361,2382,2389,2397,2431,2443,2470,2474,2561,2709,2739,2774,2796,2811,2833,2865,2893,2925,2935,2951,2964,3007,3021,3058,3090,3138,3167,3189,3291,3327,3369,3374,3410,3422,3508,3524,3580,3632,3738,3779,3798,3890,3911,3933,3946,3955,3971,3977,3989,4143,4156,4163,4175,4197,4205,4211,4352,4394,4410,4421,4465,4571,4590,4601,4609,4637,4645,4654,4753,4783,4804,4837,4854,4887,4944,4992,4998,5019,5028,5041,5047,5054,5066,5203,5230,5295,5393,5450,5476,5485,5575,5608,5639,5654,5661,5689,5704,5711,5722,5761,5787,5855,5873,5878,5929,5944,5962,6000,6058,6079,6089,6098,6109,6139,6191,6304,6315,6351,6369,6375,6411,6420,6449,6476,6515],"Deep":[3],"Learning":[4],"(DL)":[5],"methodologies":[6,476],"has":[7,205,696,776,1853,3424,6085,6227],"significantly":[8,637,1273,1313,1857,1885,3425,3849,3935,6388],"enhanced":[9,1079,2512,3922,5886,6337,6381,6557],"the":[10,17,68,91,97,180,192,196,207,226,233,243,258,267,280,305,308,358,412,444,447,456,514,538,627,640,681,698,716,740,747,781,804,841,908,923,956,975,982,1019,1031,1050,1066,1076,1085,1099,1110,1119,1124,1167,1186,1200,1211,1214,1229,1237,1269,1287,1306,1309,1315,1324,1330,1335,1345,1348,1361,1368,1381,1391,1411,1416,1419,1422,1430,1454,1482,1512,1516,1579,1590,1593,1602,1618,1623,1646,1653,1699,1707,1744,1769,1794,1807,1832,1854,1859,1908,1933,1961,1968,1983,1987,1995,2014,2020,2034,2040,2043,2049,2066,2071,2118,2131,2140,2148,2178,2193,2259,2290,2298,2311,2359,2365,2383,2387,2403,2436,2449,2494,2509,2541,2594,2707,2737,2794,2809,2831,2848,2907,2933,2952,2965,2984,2996,3001,3005,3010,3018,3022,3027,3031,3056,3059,3070,3074,3081,3085,3091,3098,3105,3111,3136,3139,3165,3182,3190,3200,3206,3248,3261,3265,3277,3289,3345,3355,3362,3367,3370,3375,3379,3387,3395,3408,3515,3525,3578,3581,3585,3596,3618,3678,3712,3762,3785,3815,3822,3829,3836,3864,3869,3875,3884,3908,3937,3953,3968,3974,4037,4052,4079,4084,4141,4153,4161,4172,4183,4203,4259,4265,4281,4284,4293,4325,4346,4350,4356,4375,4382,4406,4411,4419,4422,4427,4458,4466,4490,4498,4540,4564,4569,4588,4602,4633,4638,4643,4646,4652,4704,4750,4754,4768,4774,4781,4790,4793,4802,4805,4826,4835,4838,4885,4896,4929,4933,4996,5005,5025,5029,5039,5045,5051,5063,5096,5146,5159,5177,5213,5251,5271,5283,5286,5293,5314,5321,5330,5354,5359,5365,5370,5382,5387,5391,5394,5408,5416,5423,5483,5493,5573,5595,5605,5609,5617,5628,5640,5652,5668,5672,5692,5709,5725,5729,5739,5770,5785,5793,5804,5809,5833,5843,5853,5871,5909,5914,5926,5942,5960,5985,6020,6065,6087,6107,6137,6181,6192,6206,6302,6324,6366,6373,6400,6408,6418,6439,6447,6480,6487,6497,6513,6609],"field":[11,1745,1860],"medical":[13,314,409,466,601,665,798,1747,1862,2407,2460,2645,3450,4020,5750,5970,6075,6083,6286,6415],"Imaging,":[14],"particularly":[15,333,778,1473,2836,3950],"in":[16,46,61,74,80,93,114,159,198,213,355,376,553,622,797,928,935,1088,1123,1151,1157,1304,1456,1581,1632,1710,1739,1835,1844,1943,2013,2055,2077,2139,2176,2225,2249,2297,2406,2452,2459,2563,2582,2632,2654,2667,2689,2712,2742,2815,2822,2847,2878,2901,2909,2920,2927,3132,3161,3231,3241,3255,3400,3413,3418,3493,3514,3666,3733,3749,3776,3904,3952,3960,4006,4016,4051,4083,4098,4152,4241,4258,4340,4345,4489,4561,4592,4642,4738,4843,4908,5070,5246,5433,5445,5542,5553,5582,5594,5619,5643,5657,5744,5749,5756,5903,5969,5974,6074,6082,6106,6162,6174,6186,6199,6232,6245,6259,6284,6295,6317,6321,6362,6399,6413,6486,6530,6535,6593,6637,6648],"interpretation":[18],"chest":[20,164,488,1401,1582,1897,2080,2199,2266,2432,2564,2583,2690,2748,5546,5586,5647,6110,6218,6505],"radiographs":[21,165],"(CXRs).":[22],"Among":[23],"these":[24,115,407,504,1588,2710,3615,4950,5174,5963],"advancements,":[25],"Convolutional":[26,121,1092,1175,2328,3891,3896,3912],"Neural":[27,122,1093,1176,2329,3913],"Networks":[28,3235,3914],"(CNNs)":[29,3915],"have":[30,714,1737,1753,1884,2628,2683,5867,6342],"emerged":[31],"as":[32,393,422,626,672,836,1049,1061,1459,1994,2374,2481,2959,2981,3108,3229,3307,3416,3457,3958,4058,4096,4119,4217,4239,4272,4515,4551,4819,5132,5206,5329,5482,5696,5972,6014,6039,6125,6156],"a":[33,120,153,423,450,470,526,595,794,811,837,854,860,872,878,949,1091,1138,1222,1244,1294,1340,1437,1460,1552,1637,1740,1759,1802,1813,1876,1944,2001,2026,2058,2084,2089,2098,2111,2126,2154,2174,2189,2230,2275,2308,2315,2320,2327,2345,2771,2862,2890,3063,3129,3145,3158,3252,3294,3348,3465,3490,3498,3521,3573,3611,3629,3656,3667,3721,3746,3766,3772,3777,3901,3928,3983,4003,4180,4209,4313,4391,4661,4724,4840,4852,4888,4989,5016,5200,5227,5304,5309,5337,5517,5636,5753,5799,6008,6054,6119,6246,6334,6394,6434,6597],"paramount":[34],"technology":[35],"for":[36,65,147,200,235,293,427,516,742,809,870,882,955,987,1068,1078,1127,1213,1385,1474,1496,1503,1515,1566,1706,1749,1864,1926,2004,2092,2101,2192,2599,2622,2644,2672,2776,2889,2954,2973,3218,3314,3406,3600,4031,4132,4403,4808,5004,5057,5226,5263,5268,5316,5334,5340,5348,5358,5369,5418,5567,5615,5671,5731,5807,5836,5845,5950,5959,5997,6011,6168,6180,6214,6397,6547,6583,6599],"processing":[37,1427],"and":[38,42,88,104,239,256,322,339,343,351,367,400,404,493,610,619,631,642,651,655,659,684,694,706,726,746,757,791,806,829,886,931,1081,1140,1148,1226,1250,1254,1278,1337,1425,1465,1476,1529,1556,1599,1734,1755,1822,1870,1881,1974,2019,2051,2075,2170,2186,2218,2241,2256,2283,2300,2336,2417,2477,2504,2526,2701,2731,2755,2764,2778,2803,2826,2867,2876,2895,2913,3029,3066,3100,3163,3175,3185,3210,3226,3275,3282,3364,3441,3460,3486,3503,3530,3565,3593,3605,3639,3647,3658,3683,3826,3841,3872,3895,3916,3930,3940,3963,3980,3986,3997,4012,4022,4041,4061,4069,4135,4149,4177,4222,4230,4234,4247,4275,4287,4323,4338,4379,4424,4444,4455,4545,4622,4635,4657,4680,4711,4771,4822,4875,4891,4904,4932,4937,4958,4968,4981,5001,5060,5094,5136,5144,5189,5195,5222,5232,5239,5248,5277,5297,5336,5411,5427,5441,5460,5469,5512,5565,5578,5622,5650,5706,5735,5738,5780,5885,5913,5932,5947,5977,6072,6104,6128,6148,6267,6273,6288,6307,6319,6336,6348,6353,6473,6521,6573,6606],"Classifying":[39],"CXR":[40,112,176],"images":[41,137,1048,1126,2373,2416,2602,3920,4331,4354,4387,4572,4591,4656,5649,5751,6123],"demonstrating":[43,108,179,2222,2289,2514,6372],"exceptional":[44,168,2368,2441],"proficiency":[45],"detecting":[47,705,1147,1837,2633,5583],"COVID-19-related":[48],"signs":[49,5688],"[1].":[50],"Although":[51,6602],"Reverse":[52],"Transcription":[53],"Polymerase":[54],"Chain":[55],"Reaction":[56],"(RT-PCR)":[57],"tests":[58,421,480,483,506],"surpass":[59,6365],"CXRs":[60],"accuracy":[62,189,1101,1458,1541,1687,2234,2395,2442,2746,3487,3646,3682,3996,4957,5247,5405,5421,5670,5902,5912,5931,5949,6138,6313,6367,6529,6611],"And":[63],"reliability":[64,618,4337,6582],"virus":[66,92,448],"detection,":[67,3440],"latter":[69],"remains":[70],"an":[71,133,264,867,1062,1776,2204,2393,2428,2440,3149,3238,3311,3707,3728,4516,4786,6096,6312],"ubiquitous":[72],"tool":[73,6396],"clinical":[75,1849,2817,2922,4341,6012,6234,6260,6296,6537,6632,6646],"practice":[76],"[2].":[77],"RT-PCR":[78],"excels":[79],"early":[81,428,682,743],"detection":[82,683,745,885,903,2194,2535,2581,2745,3459,5544,5621,5645,6216],"capabilities,":[83,2370],"enabling":[84,266,680,719,826,1768,4009],"prompt":[85],"treatment":[86,340,751,2765],"initiation,":[87],"uniquely":[89],"identifies":[90,2596],"asymptomatic":[94],"individuals":[95,1820,2161],"through":[96,455,1185,1481,1892,2215,2517,2932,2990,3288,3394,3727,3883,6145,6558],"analysis":[98,309,457,486,567,1439,1895,2124,2519,2934,3932,4015,4991,6143,6179],"of:":[99],"Saliva":[100],"samples,":[101,103,107,5042],"throat":[102],"nasal":[105],"passage":[106],"superior":[109,5679],"performance":[110,1555,1613,1660,2211,2340,2369,2447,2496,2912,3502,3620,3945,4204,4737,4954,5242,5385,5449,5532,5559,5680,5700,5703,6132,6244],"over":[111,1543,3062,5012,6195],"evaluations":[113],"aspects.A":[116],"recent":[117],"investigation":[118,1414,1939],"employed":[119,1084,1243,1642,4278,4412,4747,4832,5102,6545],"Network":[123,1094,1177,2330],"(CNN)":[124,1095,1178,2331],"model":[125,812,881,1096,2366,2438,2542,3484,3582,3645,3669,3681,3725,3753,3768,3786,4228,4249,4266,4459,4562,4659,4705,4727,4993,5009,5074,5112,5160,5178,5214,5302,5396,5420,5477,5591,5625,5733,5776,5948,6023,6049,6183,6194,6226,6258,6459],"to":[126,186,241,254,396,442,490,501,524,563,593,607,616,722,764,853,916,1002,1097,1117,1136,1181,1221,1257,1262,1354,1367,1403,1492,1510,1569,1572,1643,1656,1662,1681,1716,1722,1743,1766,1856,2079,2333,2413,2492,2498,2532,2557,2579,2649,2686,2760,2784,2839,2916,2987,3016,3148,3245,3280,3316,3347,3358,3381,3436,3449,3482,3558,3643,3691,3694,3787,3791,3839,3845,3918,3993,4066,4078,4091,4106,4109,4201,4254,4279,4296,4300,4311,4320,4328,4390,4440,4450,4460,4485,4522,4538,4631,4672,4688,4698,4707,4723,4748,4800,4825,4833,4861,4869,4878,4899,4913,5109,5124,5140,5171,5184,5210,5218,5257,5270,5312,5464,5492,5538,5627,5634,5763,5797,5862,5908,5965,6018,6205,6240,6255,6262,6292,6383,6407,6424,6457,6469,6539,6565,6615],"distinguish":[127],"between:The":[128],"CNN":[129,1336,2641,3168,3221,3767],"model,":[130,2781,2835,6525],"trained":[131,3770,4597,5011,5765,5882,5892,6117],"on":[132,163,245,319,901,910,1233,1275,1399,1429,1609,1666,1782,1812,1964,2037,2274,2427,2484,3584,3771,4048,4208,4462,4505,4527,4785,4949,4956,4974,5149,5179,5386,5422,5691,5716,5724,5766,5864,5870,5880,5893,5935,6048,6064,6118,6136,6270,6323,6333,6622],"extensive":[134,2604],"dataset":[135,1463,1708,1761,1815,2276,2346,2430,2465,2769,2789,2900,2941,2953,2967,4210,4423,4530,4565,5142,5717,5727,5736,6121,6170,6503,6576],"comprising":[136,2347,6122],"from:\u2022":[138],"Patients":[139,1792],"diagnosed":[140],"with":[141,520,545,646,709,789,981,1014,1090,1174,1352,1821,1922,2048,2130,2229,2326,2353,2529,2714,3004,3205,3475,3880,3921,3973,4405,4497,4936,5073,5273,5308,5883,6034,6053,6141,6483],"pneumonia":[142,224,387,433,1891,2197,2228,2264],"\u2022":[143,272,378,382,481,484,542,961,963,965,991,1252,1791,1914,1916,1951,1953],"Those":[144],"testing":[145,1804,1829],"positive":[146,1376,1789,2377,4164,5868],"COVID":[148],"-19":[149],"Healthy":[150],"subjects":[151],"achieved":[152,454,2439],"remarkable":[154,710,2400,2446,3944],"precision":[155,171,1184,1431,1834,2451],"rate":[156,1620,1639,1648,1719,2396,5342,6314],"97.6%":[158],"identifying":[160,583,871,929,2226,2262],"COVID-19":[161,363,884,902,930,1890,1927,2196,2227,2281,2376,2454,2534,2562,2580,2617,2634,2687,2744,2797,2926,3956,4223,5543,5584,5620,5644,5989,6207,6215,6516,6631],"cases":[162,2282,2284,2473,2798,4157,6517],"(CXR).":[166],"This":[167,277,330,774,875,1017,1266,1300,1388,1435,1796,1851,2399,2445,2551,2638,2870,3077,3214,3402,3532,3851,3924,3999,4399,4575,4626,4676,4756,5197,5570,5611,5901,6002,6093,6275],"level":[169,597],"surpasses":[172],"that":[173,503,1312,1374,1409,1550,2168,2277,2610,2659,2883,3180,3199,3298,3386,3469,3576,4166,4728,5212,5665,5866,5890,6224,6551],"traditional":[175,2499,2718,3650],"diagnostic":[177,188,237,425,472,522,557,590,599,623,647,717,1100,1128,1142,1183,1457,1499,1772,1846,1872,1924,2419,2549,5757,5967,6091,6152,6391],"techniques,":[178,4056,6037],"potential":[181,1842,1855,2405,2458,2738,2915,4487,4713,5122,5521,6268,6419,6635],"deep":[183,2294,2677,2724,3242,3257,3414,3430,5991,6080,6421],"learning":[184,633,1619,1638,1647,1700,1718,2295,2575,2681,2720,2725,3243,3412,3759,3800,5341,5992,6081,6100,6114,6332,6422],"algorithms":[185,702,2682,2711,3744],"improve":[187,1098,2533,2917,4070,4324,4632,5966],"[3].":[190],"In":[191,667,944,1038,1319,1692,1875,1978,2146,2307,3152,3761,6203],"current":[193,5606,5641,6481,6493],"healthcare":[194,211,246,559,720,762,911,939,2880,6248,6426],"landscape,":[195,718],"surge":[197],"demand":[199],"intensive":[201],"care":[202],"units":[203],"(ICUs)":[204],"exposed":[206],"capacity":[208,741],"constraints":[209,3609],"systems":[212,733],"several":[214,1506,5099,6443],"developed":[215,1754,2188,2312,6340],"countries.":[216],"influx":[218],"patients":[220,1790,2096,2109,2894,5231],"suffering":[221],"from":[222,614,674,813,1329,1344,1622,1785,1819,2265,2545,2635,2841,2861,4308,4715,4789,5164,5282,5381,5585,6433,6466],"COVID-19-induced":[223],"into":[225,971,1026,1932,2944,3741,3868,4534,4925,5533,5792,5984,6280],"ICU":[227],"underscores":[228,2402,5708],"this":[229,431,439,464,534,1039,1132,1504,1711,1828,2853,2902,2928,3155,3194,3286,3341,3947,4198,5658],"pressing":[230],"challenge,":[231],"highlighting":[232,513,863,1203,1380,1628,1840,5803],"need":[234,1067,5730,6598],"effective":[236,2597,2762,4011,5951],"tools":[238,580,760],"strategies":[240],"manage":[242],"burden":[244],"resources":[247,807,1277,1537],"[4].The":[248],"system":[249,306,2313],"employs":[250],"relational":[251,301],"feature":[252,302,926,1070,1113,1216,1325,1365,2334,2729,3093,3141,3186,3297,4499,5846,5857],"intelligence":[253,303,572,731],"analyze":[255,4524],"interpret":[257,2995],"interactions":[259,312],"among":[260,2159,4283],"various":[261,687,2571,2679,2955,3427,4017,4708,4902,4945,5451,5767],"elements":[262,326],"within":[263,313,449,586,866,1848,3073,3429,4684,5158],"image,":[265,4378,4770],"assessment":[268],"of:\u2022":[269,282,415],"Spatial":[270],"relationships":[271,286,321],"Dynamics":[273],"between":[274,324,1554,3501,3680,4039,4182,5772],"different":[275,325,824,1573,2801,4442,4474,4716,4871,5115,6285,6467,6519],"components":[276],"capability":[278,331,6382],"facilitates":[279,3299],"evaluation":[281,2273,2381,4943,4973,4986,5113,5734,6097],"Tumor-tissue":[283],"interactions\u2022":[284],"Inter-organ":[285],"(e.g.,":[287,4416],"heart-lung":[288],"interactions)":[289],"which":[290,452,548,1006,1396,1575,2806,3519,4261,4493,5118,5168,5430,5556,6236],"is":[291,332,441,453,499,537,914,941,1059,1284,1302,1576,2930,2942,2957,3039,3055,3128,3203,3216,3366,3404,3481,3534,3572,3628,3689,3769,3828,3848,3949,4029,4075,4171,4374,4381,4401,4758,5038,5044,5050,5062,5182,5208,5290,5480,5504,5613,5633,5742,5752,5831,5832,5852,5860,5905,6172,6184,6197],"crucial":[292,500,915,1303,1577,1630,3159,3405,3630,4807,4841,5183],"diagnosing":[294,707],"conditions":[295,347,2844],"like:\u2022":[296],"Heart":[297],"failure\u2022":[298],"Pulmonary":[299,1954],"embolismThe":[300],"enables":[307,761,1109,1390,3927],"complex":[311,584,787],"images,":[315,587,1153,1478,2434,2555,3934,3957,4213,4492,4686,6507],"providing":[316,769,1929,4179,6051,6393],"valuable":[317,1010,5982,6278,6395],"information":[318,3028,3882],"spatial":[320,2661,3019,3969,4867],"dynamics":[323],"[5]":[327],"[6]":[328],"[7].":[329],"beneficial":[334],"in:\u2022":[335],"Oncology":[336],"(accurate":[337],"diagnosis":[338,528,662,1976,2301,2763,2924,6272],"planning)\u2022":[341],"Cardiovascular":[342],"pulmonary":[344,352,1935,1965,1981,2030,2063,2182,2936],"diseases":[345,708],"(diagnosing":[346],"like":[348,1527,5561,6453,6561,6642],"heart":[349],"failure":[350],"embolism)As":[353],"depicted":[354,3417,6161],"Figure":[356,3232,3419,3734,3961,5745,5975,5978,6163,6175,6187],"1,":[357],"radiographic":[359],"features":[360,1234,1905,2354,2986,2998,3072,3789,4683,5865],"characteristics":[361,1420],"typically":[364],"encompass:\u2022":[365],"Bilateral":[366,1911],"lower-zone":[368],"dominant":[369,402],"ground-glass":[370,1912],"opacities":[371,1913],"(GGOs)\u2022":[372],"Consolidations,":[373],"predominantly":[374,2137],"peripheral":[375],"distribution":[377,2028,2061,2180,4282,4549],"Interlobular":[379,1917],"septal":[380,1918],"thickening":[381],"Pleural":[383,1949],"effusionsIn":[384],"contrast,":[385,1693,2147,4508],"viral":[386,2287,6129],"caused":[388],"by":[389,570,663,1684,1801,2727,3000,3097,3110,3361,3755,4472,4923,5320,5507,5698],"non-SARS-CoV-2":[390],"viruses,":[391],"such":[392,477,625,671,3456,4057,4216,4271,4818,4856,5131,6038],"influenza-A,":[394],"tends":[395],"exhibit:\u2022":[397],"Unilateral,":[398],"central,":[399],"upper-zone":[401],"GGOs":[403],"consolidations":[405],"Using":[406,2656],"distinctions,":[408],"experts":[410],"advocate":[411],"concurrent":[413],"use":[414,3290],"Chest":[416],"radiography\u2022":[417],"Nucleic":[418],"acid":[419],"amplification":[420],"primary":[424,436,1020,1051,2946,3479,4195],"strategy":[426,3468],"identification":[429,977,2560,4997],"novel":[432,3846],"strain.":[434],"objective":[437,3480,4196],"examination":[440,536],"detect":[443],"presence":[445],"patient,":[451],"specific":[459,535,873,1264,1765,3597,3773,5125,5150],"biomarkers":[460],"[8].":[461],"To":[462,1634,2851,4225,4650,6025,6250],"augment":[463],"assessment,":[465],"practitioners":[467,763],"often":[468,3267,5169,5995],"employ":[469],"multimodal":[471],"approach,":[473,1133,2310],"incorporating":[474,2859,3833,6638],"additional":[475,1941,4939,6559,6639],"as:\u2022":[478],"Antibody":[479],"Antigen":[482],"Radiographic":[485],"via":[487,904,2198],"X-rays":[489,1402,2565],"verify":[491],"infection":[492,1795,2669],"facilitate":[494,820,3454,3982,4539],"accurate":[495,725,830,1141,1498,2559,2887,4690,5224],"diagnosis.":[496],"However,":[497,6220],"it":[498,913,1781,2884,5207,5537,5601,5812,6508],"acknowledge":[502,2785],"supplementary":[505],"may":[507,549,2172,2790,6016,6509,6590,6626],"not":[508,2791,2845,3434,6228,6358,6510,6627],"always":[509],"yield":[510],"precise":[511,976,5936],"outcomes,":[512,2083],"necessity":[515],"their":[517,1003,1259,1567,1938,2236,2756,3446,4049,4953,5998,6390],"combined":[518],"application":[519,1483,2291,2388,3447,5760,5877],"other":[521,1544,5539,5683,6035],"techniques":[523,1428,2296,2598,2613,2693,2741,3474,4236,4251,4430,4797,4830,5100,5130,6452,6485,6560],"ensure":[525,617,1511,3198,3610,4226,4245,4559,4699,5110,5211],"definitive":[527],"[9]":[529],"[10].A":[530],"significant":[531,795,998,1535,1612,1741,2007,2099,2630,3087,3253,3398,3747,4004,5518,6009],"limitation":[532],"considerable:\u2022":[539],"Processing":[540],"time":[541,641,805,1280],"Expenses":[543],"associated":[544,645],"its":[546,551,988,1629,1841,2223,2362,2411,2457,2515,2786,2914,4336,4525,4906,5187,5580,5699,6243,6264],"execution,":[547],"hinder":[550],"applicability":[552,6283],"resource-constrained":[554,6594],"settings.To":[555],"minimize":[556],"errors,":[558],"professionals":[560,721],"are":[561,1378,1397,1531,1564,2971,3014,3069,3095,3177,4064,4117,5379,5431,5551,5842,5994,6160],"advised":[562],"utilize":[564,1323],"automated":[565,6090],"imaging":[566,893,1584,1748,1863,2082,2408,2461,2804,2827,2868,4021,4717,4733,5971,6084,6522,6624,6640],"software,":[568],"powered":[569],"artificial":[571],"(AI),":[573],"when":[574,772,1012,1617,2837],"interpreting":[575],"X-ray":[576,892,905,1047,1125,1152,1475,1583,2267,2433,2601,2636,2938,4212,4353,4377,4491,4685,5547,5648,6111,6506,6623],"photographs.":[577],"These":[578,712,818,1358,2165,2269,2692,2734,2993,3782,4317,4719,4882,6043,6356,6489],"advanced":[579,1886,2293,3472,6450],"excel":[581],"at":[582,1518,5007,5397],"patterns":[585,2653,3844],"thereby":[588,1074,1154,3025,3318,3496,4640,4894],"improving":[589,1867,3936,4506,4905],"accuracy.":[591,620,1691,2536],"Additionally,":[592,1030,2006,4692,5127],"maintain":[594],"high":[596,1685,2002,2231,2665,6528],"precision,":[600,5563],"staff":[602],"should":[603,2857,6441],"take":[604],"periodic":[605],"breaks":[606],"avoid":[608],"fatigue":[609],"seek":[611],"second":[612],"opinions":[613],"colleagues":[615,2187],"Innovations":[621],"methodologies,":[624],"integration":[628,1106,3737,3889,6448],"AI":[630,654,695,2740],"machine":[632,2680,2719,3750],"(ML)":[634],"technologies,":[635,676],"can":[636,657,734,785,2614,2722,2885,2905,3197,3329,3389,3801,3853,4729,5215,5598,5813,6006,6069],"reduce":[638,3017,4321,4425,5145],"both":[639,2216,2279,5456],"financial":[643],"costs":[644],"processes,":[648,2221],"enhancing":[649,739,816,1845,2548,2743,3752,4335,4895],"efficiency":[650,1080,3485,3726,3939,5968],"reliability.":[652],"Specifically,":[653,6438],"ML":[656,693],"accelerate":[658],"refine":[660,1182,4673],"disease":[661,744,1433,5937,5952],"analyzing":[664,2033],"data.":[666],"addition,":[668],"digital":[669],"biomarkers,":[670],"data":[673,1423,2035,2775,2840,3225,4232,4548,4612,4624,4795,4828,5116,5128,5221,5774,5895,6607,6647],"wearable":[675],"offer":[677,2664,6045],"real-time":[678],"information,":[679],"monitoring":[685,759,1973],"health":[688,767],"conditions.":[689,1585,4476],"synergy":[691],"facilitated":[697,6086],"development":[699,6088],"sophisticated":[701],"capable":[703,1144],"precision.":[711,817,5782],"advancements":[713,5618],"transformed":[715],"make":[723,1007,3227],"more":[724,827,1497,1547,2114,2558,2761,3322,3984,4010,4314,4689,4725,4810,4845,4889,5896,6055,6435],"timely":[727,770,1975],"diagnoses":[728,6171],"[11].":[729],"Artificial":[730],"(AI)-empowered":[732],"efficiently":[735,2615],"process":[736,843,1077,1267,3223,3797,3852,3919,4348,4815,4850,5313,5987],"vast":[737],"datasets,":[738,1574,4951],"formulation":[748],"customized":[750],"plans.":[752],"advent":[754,6078],"telemedicine":[756],"remote":[758],"oversee":[765],"patient":[766,1868,2918,6271],"remotely,":[768],"interventions":[771],"necessary.":[773],"approach":[775,1165,2552,2751,3533,3903,3948,4001,4628,5577,5656],"been":[777,2684,6230],"advantageous":[779],"during":[780,3104,3794,4433,4519,5033,5103],"pandemic.":[782],"Pre-trained":[783],"models":[784,802,819,954,996,1025,1035,1502,1526,1563,2247,2270,2577,2589,2726,3222,4207,4595,4946,5000,5234,5452,5541,5714,5764,5881,5891,5993,6115,6306,6341],"address":[786,1635,2852,3285,4255,6026,6251,6442,6492],"issues":[788],"speed":[790],"accuracy,":[792,1597,4660,5478,5510,5514,5562,5777],"offering":[793,1539],"advantage":[796],"diagnostics.":[799],"Leveraging":[800],"pre-trained":[801,995,1024,1247,3824],"reduces":[803,1036,2543],"required":[808],"training":[810,1279,1675,1686,1780,1797,2363,2777,2849,3256,3764,4229,4809,4844,4967,5347,5509,5773,6318,6347],"scratch,":[814],"while":[815,3083,3621,4303,6495,6501],"knowledge":[821],"transfer":[822,880,3758,3799,6099,6331],"across":[823,2065,2800,3614,3661,4093,4332,4573,4731,4901,4961,5114,5191,5250,5407,5681,6463,6518],"domains,":[825],"efficient":[828,1548,3708,4013],"solutions.":[831],"Grad-CAM":[833,858,970,1086,1108,1282,1290,1394,5762,5783,5879,5980,6033,6546],"algorithm":[834,2191],"serves":[835],"visualization":[838,887,1111],"tool,":[839],"elucidating":[840],"decision-making":[842,1317,2420,5986],"convolutional":[845,1332,2250,2657,2969,3002,3106,3133,3473,3709,5795],"neural":[846,2251,3258,4413,5287],"networks":[847],"(CNNs).":[848],"By":[849,1130,1586,2537,2897,3192,3832,3966,5588,6061,6327],"utilizing":[850,1131],"gradients":[851,1346,3266,3315,5786],"pertaining":[852],"selected":[855,1562],"target":[856,1223,5789],"class,":[857,5255],"generates":[859],"localization":[861,925,1201,5748,5781,5801,5834,5904,5953],"map":[862,1202,1217,1231,1366,3094,3142,5802,5835,5847],"pivotal":[864,3722],"areas":[865,890,6387],"image":[868,1311,1426,1466,1471,1494,2201,2646,3208,3437,3635,3687,3990,4014,4528,4611,4618,4693,4701,4709,4775,4788,5151,5778,5915,5945],"critical":[869,1407,1930,3178,4402,4616,5659,5754],"concept.":[874,5810],"study":[876,2522,2569,2595,2705,2929,5957,6210,6405],"introduces":[877,3464],"comprehensive":[879,1760,1893,3929,4627,4890,4942,5637,6056,6120,6436],"synchronous":[883],"affected":[889,979,1998,2138,6386],"using":[891,1286,2344,2747,3193,4355,4598,4620,4915,5015,5455,5545,5646,5954,6217],"[12].":[894],"Unlike":[895,3649],"prior":[896],"research,":[897,1505],"our":[898,945,972,1027,1057,1103,1163,1263,1320,1887,1958,2768,2780,2812,2834,2873,2899,2910,3153,4610,5258,5530,5576,5590,5624,5655,6209,6225,6238,6257,6281,6339,6376,6379,6502,6552,6620],"work":[899,2856,6029],"focuses":[900,1232,4504],"images.":[906,2268,2637,2939,4755,5548,6112],"Given":[907],"pressure":[909],"systems,":[912],"leverage":[917],"every":[918],"available":[919,1758,3586],"resource":[920],"efficiently.":[921],"Integrating":[922],"Grad-Cam":[924],"aids":[927],"assessing":[932,4952],"severity,":[933],"assisting":[934,1155],"determining":[936,2177,3135],"whether":[937],"immediate":[938],"intervention":[940],"needed.":[942],"[13]":[943],"investigation,":[946],"we":[947,1041,1134,1242,1322,2904,3196,5535,5597,6068,6221,6253,6544,6549,6603],"leveraged":[948,1042,2314],"diverse":[950,2879,4732,4892,5228,6464],"range":[951,2864,2892,4853,5229],"pretrained":[953,1034,5280,6305],"classification":[957,1240,2531,2674,2688,3743,3954,4033,4206,4541,4648,6108],"task,":[958],"including:\u2022":[959,1910,1948],"ResNet34":[960,1249],"ResNet50":[962],"EfficientNet-B4":[964,1253],"EfficientNet-B5":[966],"architecturesThe":[967],"incorporation":[968],"methodology":[973,3926],"enabled":[974,4884,4988],"regions,":[980,2540,2825],"EfficientNet":[983,3462,3705,6103,6182],"architecture":[984,1055,3166,3202,3240,3463,5161],"being":[985,2136,5069],"utilized":[986,2464],"exceptional:\u2022":[989],"Efficiency":[990],"EffectivenessThe":[992],"utilization":[993],"offers":[997,1592,2752],"advantages,":[999],"primarily":[1000],"due":[1001,1661],"pre-learned":[1004],"weights,":[1005,3825],"them":[1008,2620,5267],"exceptionally":[1009],"even":[1011,2060],"working":[1013],"limited":[1015,1667,2715,3435],"datasets.":[1016],"constitutes":[1018],"benefit":[1021,6432],"integrating":[1023,2319],"investigative":[1028,2237],"approach.":[1029,6377],"employment":[1032,1469],"the:":[1037],"study,":[1040,1321,1712,1878,2238,3154],"publicly":[1043,1757],"accessible":[1044],"datasets":[1045,2860,3847,4910,5181,5429,5459,5463,5684,5768,5888,6465],"experimental":[1052,4347,6531],"medium.":[1053],"experiment":[1058,4199],"designed":[1060,2643,3015,3244,4630],"end-to-end":[1063],"system,":[1064,1143],"eliminating":[1065],"manual":[1069,2605],"extraction":[1071,2335,2730,3187,4500],"or":[1072,2378,2843,3321,6041,6455,6563,6645],"selection,":[1073],"streamlining":[1075,1871],"effectiveness.":[1082],"We":[1083,5414],"technique":[1087,1173,4662,4757],"conjunction":[1089],"system.":[1104],"importance,":[1114],"allowing":[1115,2671],"us":[1116,6290],"identify":[1118,1406,2650,5120],"most":[1120,1382,1996,3032,3086],"relevant":[1121,1383,6630],"regions":[1122,1204,1307,1384,1408,4464,5806],"decision-making.":[1129],"aimed":[1135,4319],"develop":[1137],"robust":[1139,2673,4726,5111],"automatically":[1146],"localizing":[1149],"abnormalities":[1150],"clinicians":[1156,1405,6015,6052],"making":[1158,2619,4703],"informed":[1159],"decisions.The":[1160],"core":[1161,3507],"methodological":[1164],"integrates":[1166,5303],"Gradient-weighted":[1168],"Class":[1169,4290],"Activation":[1170],"Mapping":[1171],"(Grad-CAM)":[1172],"architectures,":[1179],"aiming":[1180,1491],"following":[1187,2054,3112,4428,5022],"formulation:\u25a1\u25a1":[1188],"\u25a1\u25a1\u25a1\u25a1\u25a1\u25a1\u25a1\u25a1-\u25a1\u25a1\u25a1\u25a1\u25a1\u25a1":[1189,5817,5829],"=":[1190,1291,1444,3045,3114,3121,3334,3540,3547,3553,3807,3858,4102,4186,4368,4581,5077,5499,5524,5819],"\u25a1\u25a1\u25a1\u25a1\u25a1\u25a1\u25a1\u25a1(\u2211":[1191,5820],"\u25a1\u25a1":[1192,1193,1194,1195,1196,1197,1207,1208,1209,3047,3050,3052,3054,3067,3337,3352,3809,5489,5490,5496,5497,5500,5501,5502,5525,5818,5821,5822,5823,5824,5825,5826,5828,5830,5850,5851],")(1)whereLGrad-CAM":[1198],"represents":[1199,3344,3814,3874,3900,4123,4773,5024],"interest,":[1206],"denotes":[1210,3354,3821,3863,4127,4587,4767],"weights":[1212,1339,1359,3783,3817,5281,5357,5368,5378,5844],"k-th":[1215,5856]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-14T06:11:07.267592","created_date":"2025-10-10T00:00:00"}
