{"id":"https://openalex.org/W3011862785","doi":"https://doi.org/10.1117/12.2550007","title":"Performance investigation of deep learning versus classifier for polyp differentiation via texture features","display_name":"Performance investigation of deep learning versus classifier for polyp differentiation via texture features","publication_year":2020,"publication_date":"2020-03-16","ids":{"openalex":"https://openalex.org/W3011862785","doi":"https://doi.org/10.1117/12.2550007","mag":"3011862785"},"language":"en","primary_location":{"id":"doi:10.1117/12.2550007","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2550007","pdf_url":null,"source":{"id":"https://openalex.org/S4306519512","display_name":"Medical Imaging 2020: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Computer-Aided Diagnosis","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/A5066885001","display_name":"David Liang","orcid":"https://orcid.org/0000-0002-7343-7583"},"institutions":[{"id":"https://openalex.org/I117030601","display_name":"Tompkins Cortland Community College","ror":"https://ror.org/051awsb83","country_code":"US","type":"education","lineage":["https://openalex.org/I117030601"]},{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Liang","raw_affiliation_strings":["Stony Brook Univ. (United States)","Ward Melville High School (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stony Brook Univ. (United States)","institution_ids":["https://openalex.org/I59553526"]},{"raw_affiliation_string":"Ward Melville High School (United States)","institution_ids":["https://openalex.org/I117030601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100652804","display_name":"David Wang","orcid":"https://orcid.org/0000-0002-0827-196X"},"institutions":[{"id":"https://openalex.org/I4210110142","display_name":"Syosset Hospital","ror":"https://ror.org/024gbbh50","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1302444339","https://openalex.org/I4210110142"]},{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Wang","raw_affiliation_strings":["Stony Brook Univ. (United States)","Syosset High School (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stony Brook Univ. (United States)","institution_ids":["https://openalex.org/I59553526"]},{"raw_affiliation_string":"Syosset High School (United States)","institution_ids":["https://openalex.org/I4210110142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000216315","display_name":"Alice C. Wei","orcid":"https://orcid.org/0000-0002-2505-959X"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alice Wei","raw_affiliation_strings":["Staten Island Technical High School (United States)","Stony Brook Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Staten Island Technical High School (United States)","institution_ids":[]},{"raw_affiliation_string":"Stony Brook Univ. (United States)","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070315477","display_name":"Yeseul Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110142","display_name":"Syosset Hospital","ror":"https://ror.org/024gbbh50","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1302444339","https://openalex.org/I4210110142"]},{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yeseul Choi","raw_affiliation_strings":["Stony Brook Univ. (United States)","Syosset High School (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stony Brook Univ. (United States)","institution_ids":["https://openalex.org/I59553526"]},{"raw_affiliation_string":"Syosset High School (United States)","institution_ids":["https://openalex.org/I4210110142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100452871","display_name":"Shu Zhang","orcid":"https://orcid.org/0000-0003-3658-3159"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shu Zhang","raw_affiliation_strings":["Stony Brook Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stony Brook Univ. (United States)","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005714545","display_name":"Marc J. Pomeroy","orcid":"https://orcid.org/0000-0002-3982-1776"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marc J. Pomeroy","raw_affiliation_strings":["Stony Brook Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stony Brook Univ. (United States)","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079022788","display_name":"Perry J. Pickhardt","orcid":"https://orcid.org/0000-0002-5534-8202"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Perry J. Pickhardt","raw_affiliation_strings":["Univ. of Wisconsin-Madison (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Wisconsin-Madison (United States)","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0336799,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"116","last_page":"116"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9916999936103821,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9916999936103821,"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":0.9549000263214111,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9193000197410583,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7636333703994751},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7622336149215698},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7613763809204102},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7437765598297119},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6967753767967224},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6900762915611267},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6018794775009155},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.519506573677063},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.4953676462173462},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4427659213542938},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24340704083442688},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21148917078971863}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7636333703994751},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7622336149215698},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7613763809204102},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7437765598297119},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6967753767967224},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6900762915611267},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6018794775009155},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.519506573677063},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.4953676462173462},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4427659213542938},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24340704083442688},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21148917078971863}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2550007","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2550007","pdf_url":null,"source":{"id":"https://openalex.org/S4306519512","display_name":"Medical Imaging 2020: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4385649027","https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3171520305","https://openalex.org/W2889302474","https://openalex.org/W2266938806","https://openalex.org/W2005234362","https://openalex.org/W1997235926","https://openalex.org/W2565656575"],"abstract_inverted_index":{"Computer-aided":[0],"diagnosis":[1],"(CADx)":[2],"of":[3,23,55,111,116,159,181,188,194,244],"polyps":[4,122],"is":[5,65],"essential":[6],"for":[7,35,175],"advancing":[8],"computed":[9],"tomography":[10],"colonography":[11],"(CTC)":[12],"with":[13,156,191,228],"diagnostic":[14],"capability.":[15],"In":[16],"this":[17],"paper,":[18],"we":[19,41,86,209],"present":[20],"a":[21,53,74,157,192],"study":[22,232],"investigating":[24],"the":[25,80,88,95,100,109,114,131,179,186,218,222],"performance":[26,180,225],"between":[27],"deep":[28],"learning":[29],"and":[30,99,136,145,163,173,198],"Random":[31],"Forest":[32],"(RF)":[33],"classifier":[34,138,150,204],"polyp":[36,89,176],"differentiation":[37],"in":[38,151,170],"CTC.":[39],"First,":[40],"conducted":[42],"feature":[43,171],"extraction":[44,172],"via":[45],"an":[46],"extended":[47],"Haralick":[48],"model":[49,135,155,216],"(eHM)":[50],"to":[51,67,79,220,238,241],"build":[52],"total":[54],"30":[56],"texture":[57,97,102],"features.":[58],"The":[59],"gray":[60],"level":[61],"co-occurrence":[62],"matrix":[63,76],"(GLCM)":[64],"generated":[66],"encode":[68],"3D":[69],"CT":[70],"image":[71],"information":[72,235],"into":[73,91],"2D":[75],"as":[77],"input":[78],"convolutional":[81],"neural":[82],"network":[83],"(CNN).":[84],"Then,":[85],"split":[87],"classification":[90,223],"two":[92],"state-of-the-art":[93],"frameworks:":[94],"eHM":[96],"features/RF":[98],"GLCM":[101],"matrices/CNN.":[103],"We":[104],"evaluated":[105],"their":[106],"performances":[107],"by":[108,124,129],"merit":[110],"area":[112],"under":[113],"curve":[115],"receiver":[117],"operating":[118],"characteristic":[119],"using":[120],"1,278":[121],"(confirmed":[123],"pathology).":[125],"Results":[126],"demonstrated":[127,213],"that":[128,214],"balancing":[130],"data,":[132],"both":[133],"CNN":[134,154,182,215],"RF":[137,149,203],"can":[139],"learn":[140],"or":[141],"analyze":[142],"features":[143],"effectively,":[144],"achieve":[146],"high":[147],"performance.":[148],"general":[152],"outperformed":[153],"gain":[158,193,207],"6.4%":[160],"(balanced":[161,196],"datasets)":[162,197],"5.4%":[164],"(unbalanced":[165,200],"datasets),":[166,201],"showing":[167],"its":[168],"effective":[169],"analysis":[174],"differentiation.":[177],"However,":[178],"got":[183],"improved":[184],"through":[185],"addition":[187],"new":[189],"data":[190],"3.6%":[195],"3.4%":[199],"whereas":[202],"showed":[205],"no":[206],"when":[208,226],"enlarged":[210],"datasets.":[211],"This":[212,231],"have":[217],"potential":[219],"improve":[221,242],"task":[224],"dealing":[227],"larger":[229],"dataset.":[230],"provided":[233],"valuable":[234],"on":[236],"how":[237],"design":[239],"experiments":[240],"CADx":[243],"polyps.":[245]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
