{"id":"https://openalex.org/W4409441593","doi":"https://doi.org/10.1145/3711507.3717927","title":"Classification of Colorectal Polyps from Narrow-Band Imaging Using Deep Learning","display_name":"Classification of Colorectal Polyps from Narrow-Band Imaging Using Deep Learning","publication_year":2024,"publication_date":"2024-11-22","ids":{"openalex":"https://openalex.org/W4409441593","doi":"https://doi.org/10.1145/3711507.3717927"},"language":"en","primary_location":{"id":"doi:10.1145/3711507.3717927","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711507.3717927","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711507.3717927","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd International Symposium on Computing and Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711507.3717927","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106106295","display_name":"Sinee Chaiyarin","orcid":"https://orcid.org/0009-0001-2074-5611"},"institutions":[{"id":"https://openalex.org/I4210106686","display_name":"Chulabhorn Hospital","ror":"https://ror.org/01qc5zk84","country_code":"TH","type":"healthcare","lineage":["https://openalex.org/I4210106686"]}],"countries":["TH"],"is_corresponding":true,"raw_author_name":"Sinee Chaiyarin","raw_affiliation_strings":["Chulabhorn Royal Academy, Bangkok, Thailand"],"raw_orcid":"https://orcid.org/0009-0001-2074-5611","affiliations":[{"raw_affiliation_string":"Chulabhorn Royal Academy, Bangkok, Thailand","institution_ids":["https://openalex.org/I4210106686"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054485245","display_name":"Worrawat Sangwipasnapaporn","orcid":"https://orcid.org/0000-0003-3090-0811"},"institutions":[{"id":"https://openalex.org/I4210106686","display_name":"Chulabhorn Hospital","ror":"https://ror.org/01qc5zk84","country_code":"TH","type":"healthcare","lineage":["https://openalex.org/I4210106686"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Worrawat Sangwipasnapaporn","raw_affiliation_strings":["Chulabhorn hospital, Bangkok, Thailand"],"raw_orcid":"https://orcid.org/0000-0003-3090-0811","affiliations":[{"raw_affiliation_string":"Chulabhorn hospital, Bangkok, Thailand","institution_ids":["https://openalex.org/I4210106686"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002304873","display_name":"Anya Kiattiweerasak","orcid":"https://orcid.org/0000-0002-7703-161X"},"institutions":[{"id":"https://openalex.org/I4210106686","display_name":"Chulabhorn Hospital","ror":"https://ror.org/01qc5zk84","country_code":"TH","type":"healthcare","lineage":["https://openalex.org/I4210106686"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Anya Kiattiweerasak","raw_affiliation_strings":["Chulabhorn hospital, Bangkok, Thailand"],"raw_orcid":"https://orcid.org/0000-0002-7703-161X","affiliations":[{"raw_affiliation_string":"Chulabhorn hospital, Bangkok, Thailand","institution_ids":["https://openalex.org/I4210106686"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062714164","display_name":"Kesinee Yingcharoen","orcid":null},"institutions":[{"id":"https://openalex.org/I2802083337","display_name":"Bangkok Hospital","ror":"https://ror.org/039mhfq69","country_code":"TH","type":"healthcare","lineage":["https://openalex.org/I2802083337"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Kesinee Yingcharoen","raw_affiliation_strings":["Samitivej Hospital, Bangkok, Thailand"],"raw_orcid":"https://orcid.org/0009-0001-9440-3446","affiliations":[{"raw_affiliation_string":"Samitivej Hospital, Bangkok, Thailand","institution_ids":["https://openalex.org/I2802083337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051841431","display_name":"Montri Gururatsakul","orcid":"https://orcid.org/0000-0003-3878-240X"},"institutions":[{"id":"https://openalex.org/I2800926667","display_name":"Cairns Hospital","ror":"https://ror.org/029s9j634","country_code":"AU","type":"healthcare","lineage":["https://openalex.org/I1302211766","https://openalex.org/I2800926667","https://openalex.org/I2801244131"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Montri Gururatsakul","raw_affiliation_strings":["Cairns Hospital, Cairns, Australia"],"raw_orcid":"https://orcid.org/0000-0003-3878-240X","affiliations":[{"raw_affiliation_string":"Cairns Hospital, Cairns, Australia","institution_ids":["https://openalex.org/I2800926667"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117148614","display_name":"Tippayarat Kantawong","orcid":"https://orcid.org/0000-0003-3315-288X"},"institutions":[{"id":"https://openalex.org/I4210106686","display_name":"Chulabhorn Hospital","ror":"https://ror.org/01qc5zk84","country_code":"TH","type":"healthcare","lineage":["https://openalex.org/I4210106686"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Tippayarat Kantawong","raw_affiliation_strings":["Chulabhorn hospital, Bangkok, Thailand"],"raw_orcid":"https://orcid.org/0000-0003-3315-288X","affiliations":[{"raw_affiliation_string":"Chulabhorn hospital, Bangkok, Thailand","institution_ids":["https://openalex.org/I4210106686"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074691892","display_name":"Todsaporn Fuangrod","orcid":"https://orcid.org/0000-0001-5499-802X"},"institutions":[{"id":"https://openalex.org/I4210106686","display_name":"Chulabhorn Hospital","ror":"https://ror.org/01qc5zk84","country_code":"TH","type":"healthcare","lineage":["https://openalex.org/I4210106686"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Todsaporn Fuangrod","raw_affiliation_strings":["Chulabhorn Royal Academy, Bangkok, Thailand"],"raw_orcid":"https://orcid.org/0000-0001-5499-802X","affiliations":[{"raw_affiliation_string":"Chulabhorn Royal Academy, Bangkok, Thailand","institution_ids":["https://openalex.org/I4210106686"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069981232","display_name":"Paniti Achararit","orcid":"https://orcid.org/0000-0003-2753-7580"},"institutions":[{"id":"https://openalex.org/I4210106686","display_name":"Chulabhorn Hospital","ror":"https://ror.org/01qc5zk84","country_code":"TH","type":"healthcare","lineage":["https://openalex.org/I4210106686"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Paniti Achararit","raw_affiliation_strings":["Chulabhorn Royal Academy, Bangkok, Thailand"],"raw_orcid":"https://orcid.org/0000-0003-2753-7580","affiliations":[{"raw_affiliation_string":"Chulabhorn Royal Academy, Bangkok, Thailand","institution_ids":["https://openalex.org/I4210106686"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5106106295"],"corresponding_institution_ids":["https://openalex.org/I4210106686"],"apc_list":null,"apc_paid":null,"fwci":0.2689,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.67091313,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"41","last_page":"44"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9997000098228455,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9980999827384949,"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/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/narrow-band-imaging","display_name":"Narrow-band imaging","score":0.8475843667984009},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5301395654678345},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.519333004951477},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.494759738445282},{"id":"https://openalex.org/keywords/colorectal-polyp","display_name":"Colorectal Polyp","score":0.4654126465320587},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.43542203307151794},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.30885231494903564},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2819823920726776},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.23183324933052063},{"id":"https://openalex.org/keywords/colorectal-cancer","display_name":"Colorectal cancer","score":0.20756477117538452},{"id":"https://openalex.org/keywords/colonoscopy","display_name":"Colonoscopy","score":0.17190855741500854},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.15293437242507935}],"concepts":[{"id":"https://openalex.org/C2781399487","wikidata":"https://www.wikidata.org/wiki/Q6966306","display_name":"Narrow-band imaging","level":3,"score":0.8475843667984009},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5301395654678345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.519333004951477},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.494759738445282},{"id":"https://openalex.org/C2910564736","wikidata":"https://www.wikidata.org/wiki/Q1209892","display_name":"Colorectal Polyp","level":5,"score":0.4654126465320587},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.43542203307151794},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30885231494903564},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2819823920726776},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.23183324933052063},{"id":"https://openalex.org/C526805850","wikidata":"https://www.wikidata.org/wiki/Q188874","display_name":"Colorectal cancer","level":3,"score":0.20756477117538452},{"id":"https://openalex.org/C2778435480","wikidata":"https://www.wikidata.org/wiki/Q840387","display_name":"Colonoscopy","level":4,"score":0.17190855741500854},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.15293437242507935},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.0},{"id":"https://openalex.org/C2778451229","wikidata":"https://www.wikidata.org/wiki/Q212809","display_name":"Endoscopy","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3711507.3717927","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711507.3717927","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711507.3717927","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd International Symposium on Computing and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3711507.3717927","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711507.3717927","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711507.3717927","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd International Symposium on Computing and Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4409441593.pdf","grobid_xml":"https://content.openalex.org/works/W4409441593.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W1989555529","https://openalex.org/W1990566406","https://openalex.org/W2059057721","https://openalex.org/W2065458660","https://openalex.org/W2119253852","https://openalex.org/W2163214467","https://openalex.org/W2302003756","https://openalex.org/W2790722571","https://openalex.org/W2809596283","https://openalex.org/W2964976929","https://openalex.org/W2973140425","https://openalex.org/W3008205232","https://openalex.org/W3091900404","https://openalex.org/W3178723556","https://openalex.org/W3197720841","https://openalex.org/W3199423182","https://openalex.org/W4224949151","https://openalex.org/W4230531790","https://openalex.org/W4280645326","https://openalex.org/W4396710616"],"related_works":["https://openalex.org/W2283125355","https://openalex.org/W4244807635","https://openalex.org/W2795259429","https://openalex.org/W4210579182","https://openalex.org/W2044297386","https://openalex.org/W2015296421","https://openalex.org/W1967980581","https://openalex.org/W2055902272","https://openalex.org/W2340109761","https://openalex.org/W2924888635"],"abstract_inverted_index":{"Colorectal":[0,154],"cancer,":[1],"a":[2,70,182],"leading":[3],"cause":[4],"of":[5,19,29,49,73,143,179,187,194,199,220],"cancer-related":[6],"deaths":[7],"worldwide.":[8],"It":[9],"can":[10,25],"often":[11],"be":[12],"prevented":[13],"through":[14],"early":[15],"detection":[16],"and":[17,38,61,79,88,118,170,181,196,213],"removal":[18,37],"precancerous":[20],"polyps.":[21],"Artificial":[22],"intelligence":[23],"(AI)":[24],"improve":[26],"the":[27,47,123,130,133,147,151,165,176,218],"accuracy":[28,178,193],"colorectal":[30,50],"polyp":[31,51,75,225],"evaluation,":[32],"aiding":[33],"in":[34,53,132],"decisions":[35],"about":[36],"pathology":[39,105],"examination.":[40],"This":[41,215],"study":[42],"is":[43],"aimed":[44],"to":[45,128,163],"compare":[46,164],"performance":[48],"assessments":[52],"narrow-band":[54],"imaging":[55],"(NBI)":[56],"mode":[57],"images":[58,76,131],"between":[59,168,211],"AI":[60,212,222],"experienced":[62],"endoscopist.":[63,171,214],"In":[64],"this":[65],"deep":[66,108],"learning":[67,109],"model":[68,125,169,174],"development,":[69],"retrospective":[71],"dataset":[72],"1,648":[74],"were":[77,94,120,203],"collected":[78],"divided":[80],"into":[81,227],"training":[82],"(1,198":[83],"images),":[84,87],"validation":[85],"(100":[86],"testing":[89,134,148],"sets":[90],"(350":[91],"images).":[92],"Polyps":[93],"categorized":[95],"as":[96],"adenoma":[97],"(requiring":[98],"removal)":[99],"or":[100],"hyperplastic":[101],"(non-dangerous),":[102],"confirmed":[103],"by":[104],"results.":[106],"Seven":[107],"models,":[110],"including":[111],"EfficientNetB5,":[112],"Xception,":[113],"InceptionV3,":[114],"ResNet50,":[115],"VGG16,":[116],"ResNet34":[117],"ResNeSt50,":[119],"applied.":[121],"Only":[122],"best-performing":[124],"was":[126,161],"selected":[127],"classify":[129],"sets.":[135],"Two":[136],"endoscopist":[137,190],"(who":[138],"have":[139],"over":[140],"5":[141],"years":[142],"experience)":[144],"independently":[145],"classified":[146],"set":[149],"using":[150],"NBI":[152],"International":[153],"Endoscopic":[155],"(NICE)":[156],"classification":[157,166,226],"system.":[158],"McNemar's":[159],"test":[160],"used":[162],"results":[167],"The":[172,189,201],"Xception":[173],"had":[175,191],"highest":[177],"73.43%":[180],"negative":[183],"predictive":[184],"value":[185],"(NPV)":[186],"81.09%.":[188],"an":[192,197],"69.21%":[195],"NPV":[198],"76.84%.":[200],"differences":[202],"not":[204],"statistically":[205],"significant,":[206],"with":[207],"p-value":[208],"=":[209],"0.3149,":[210],"result":[216],"shows":[217],"capability":[219],"integrating":[221],"for":[223],"automated":[224],"clinical":[228],"practice.":[229]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
