{"id":"https://openalex.org/W3158210951","doi":"https://doi.org/10.1109/icaiic51459.2021.9415270","title":"Consideration of Convolutional Neural Networks for Image Processing of Capillaries","display_name":"Consideration of Convolutional Neural Networks for Image Processing of Capillaries","publication_year":2021,"publication_date":"2021-04-13","ids":{"openalex":"https://openalex.org/W3158210951","doi":"https://doi.org/10.1109/icaiic51459.2021.9415270","mag":"3158210951"},"language":"en","primary_location":{"id":"doi:10.1109/icaiic51459.2021.9415270","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic51459.2021.9415270","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5007095236","display_name":"Hang Thi Phuong Nguyen","orcid":"https://orcid.org/0000-0002-2016-9191"},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hang Nguyen Thi Phuong","raw_affiliation_strings":["Chonnam National University, Gwangju, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chonnam National University, Gwangju, Republic of Korea","institution_ids":["https://openalex.org/I111277659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081468926","display_name":"Hieyong Jeong","orcid":"https://orcid.org/0000-0002-8135-8252"},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hieyong Jeong","raw_affiliation_strings":["Chonnam National University, Gwangju, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chonnam National University, Gwangju, Republic of Korea","institution_ids":["https://openalex.org/I111277659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043900580","display_name":"Choonsung Shin","orcid":"https://orcid.org/0000-0003-2384-4022"},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Choonsung Shin","raw_affiliation_strings":["Graduate School of Culture, Chonnam National University, Gwangju, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Culture, Chonnam National University, Gwangju, Republic of Korea","institution_ids":["https://openalex.org/I111277659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I111277659"],"apc_list":null,"apc_paid":null,"fwci":0.2981,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.46362476,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"429","last_page":"434"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12994","display_name":"Infrared Thermography in Medicine","score":0.9847000241279602,"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/T12994","display_name":"Infrared Thermography in Medicine","score":0.9847000241279602,"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.98089998960495,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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":0.9136000275611877,"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/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.835403561592102},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7772276997566223},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6463449001312256},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6161462068557739},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5510827898979187},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5258222818374634},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.4730355441570282},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4687642753124237},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45308366417884827},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.432758092880249},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.33052778244018555},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19203129410743713}],"concepts":[{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.835403561592102},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7772276997566223},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6463449001312256},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6161462068557739},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5510827898979187},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5258222818374634},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.4730355441570282},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4687642753124237},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45308366417884827},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.432758092880249},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.33052778244018555},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19203129410743713},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaiic51459.2021.9415270","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic51459.2021.9415270","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8700000047683716,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320316670","display_name":"Universal","ror":null},{"id":"https://openalex.org/F4320321198","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1054590251","https://openalex.org/W1686810756","https://openalex.org/W2009259779","https://openalex.org/W2076178025","https://openalex.org/W2094433222","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2133665775","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2206858481","https://openalex.org/W2789876780","https://openalex.org/W2955425717","https://openalex.org/W2963446712","https://openalex.org/W3009187116","https://openalex.org/W4241771140","https://openalex.org/W6684191040","https://openalex.org/W6688059459","https://openalex.org/W6762718338"],"related_works":["https://openalex.org/W2585641","https://openalex.org/W9190101","https://openalex.org/W1284803","https://openalex.org/W14516383","https://openalex.org/W10202958","https://openalex.org/W10537142","https://openalex.org/W15119441","https://openalex.org/W797409","https://openalex.org/W2582698","https://openalex.org/W10116868"],"abstract_inverted_index":{"The":[0,114],"Convolutional":[1],"Neural":[2],"Network":[3],"(CNN)":[4],"is":[5,22,26],"an":[6],"effective":[7],"algorithm":[8],"in":[9,19,36,56,69,103],"deep":[10],"learning":[11],"and":[12,34,49,78,100,131,150,173],"the":[13,16,29,81,94,108,111,119,124,129,132,135,140,143,146,163,183,206,213,217,231],"performance":[14],"which":[15],"CNN":[17,193],"brings":[18],"life":[20,71],"problem":[21],"recognized":[23],"worthily.":[24],"Tobacco":[25],"one":[27,224],"of":[28,83,96,110,139,145,186,219],"biggest":[30],"public":[31],"health":[32],"threats":[33],"results":[35,141],"8":[37],"million":[38],"deaths":[39],"every":[40],"year":[41],"through":[42,123],"cardiovascular":[43],"diseases,":[44],"lung":[45],"disorders,":[46],"cancers,":[47],"diabetes,":[48],"hypertension.":[50],"There":[51],"are":[52,65,76],"several":[53],"methods":[54,157],"used":[55],"hospitals":[57],"for":[58,142,182,212],"inspecting":[59,74],"their":[60],"own":[61],"health,":[62],"however,":[63],"they":[64],"difficult":[66],"to":[67,87,92,158],"use":[68],"daily":[70,104],"because":[72],"all":[73,138],"devices":[75],"large-scale":[77],"complex.":[79],"Thus,":[80],"purpose":[82],"this":[84],"study":[85],"was":[86,116,153,178,190,201,209],"propose":[88],"a":[89],"new":[90],"method":[91],"self-check":[93],"effect":[95],"smoking":[97],"on":[98],"capillaries":[99],"surface":[101],"skin":[102],"life,":[105],"then":[106],"evaluate":[107],"usefulness":[109],"proposed":[112],"method.":[113],"dataset":[115,215,232],"collected":[117],"from":[118,162,236],"26":[120],"human":[121,237],"subjects":[122,127,238],"capillaroscopy;":[125],"13":[126,133],"were":[128,134],"smoker":[130],"non-smoker.":[136],"Through":[137],"recognition":[144],"difference":[147],"between":[148],"smokers":[149],"non-smokers,":[151],"it":[152,189],"confirmed":[154],"that":[155,192,203],"conventional":[156],"extract":[159],"featured":[160],"points":[161,167],"edge":[164],"or":[165],"corner":[166],"such":[168],"as":[169],"ssim":[170],"(structural":[171],"similarity)":[172],"sift":[174],"(scale-invariant":[175],"feature":[176],"transform)":[177],"not":[179],"so":[180,210],"good":[181,211],"image":[184],"processing":[185],"capillaries.":[187],"However,":[188],"found":[191],"worked":[194],"well":[195],"with":[196,205,216,223],"over":[197],"80%":[198],"accuracy.":[199],"It":[200],"discussed":[202],"efficientnet":[204],"compound":[207],"scaling":[208,225],"small":[214],"comparison":[218],"resnet50,":[220],"vgg16,":[221],"densenet121":[222],"factor,":[226],"although":[227],"COVID-19":[228],"virus":[229],"affected":[230],"making":[233],"procedure":[234],"measured":[235],"directly.":[239]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
