{"id":"https://openalex.org/W4213191623","doi":"https://doi.org/10.1117/12.2611886","title":"Comparison of performance in breast lesions classification using radiomics and deep transfer learning: an assessment study","display_name":"Comparison of performance in breast lesions classification using radiomics and deep transfer learning: an assessment study","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W4213191623","doi":"https://doi.org/10.1117/12.2611886"},"language":"en","primary_location":{"id":"doi:10.1117/12.2611886","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2611886","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment","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/A5037175940","display_name":"Gopichandh Danala","orcid":"https://orcid.org/0000-0001-6857-9408"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Gopichandh Danala","raw_affiliation_strings":["Univ. of Oklahoma (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Oklahoma (United States)","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089293830","display_name":"Sai Kiran Maryada","orcid":"https://orcid.org/0000-0003-1982-1012"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sai Kiran R. Maryada","raw_affiliation_strings":["Univ. of Oklahoma (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Oklahoma (United States)","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086084647","display_name":"Huong Pham","orcid":"https://orcid.org/0000-0001-5276-7455"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huong Pham","raw_affiliation_strings":["Univ. of Oklahoma (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Oklahoma (United States)","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020798850","display_name":"Warid Islam","orcid":"https://orcid.org/0000-0002-7810-6660"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Warid Islam","raw_affiliation_strings":["Univ. of Oklahoma (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Oklahoma (United States)","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088489737","display_name":"Meredith Jones","orcid":"https://orcid.org/0000-0001-6131-4255"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Meredith Jones","raw_affiliation_strings":["Univ. of Oklahoma (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Oklahoma (United States)","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045998635","display_name":"Bin Zheng","orcid":"https://orcid.org/0000-0002-7682-6648"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bin Zheng","raw_affiliation_strings":["Univ. of Oklahoma (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Oklahoma (United States)","institution_ids":["https://openalex.org/I8692664"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5037175940"],"corresponding_institution_ids":["https://openalex.org/I8692664"],"apc_list":null,"apc_paid":null,"fwci":0.5723,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.63152456,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"35","last_page":"35"},"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":0.9998999834060669,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","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/T10862","display_name":"AI in cancer detection","score":0.9987999796867371,"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/T10696","display_name":"Gastric Cancer Management and Outcomes","score":0.9696999788284302,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.778755784034729},{"id":"https://openalex.org/keywords/radiomics","display_name":"Radiomics","score":0.7449933290481567},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7157495021820068},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6747727394104004},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6196832656860352},{"id":"https://openalex.org/keywords/cad","display_name":"CAD","score":0.607661783695221},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5878404974937439},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5129453539848328},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5033299326896667},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.4618126153945923},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44655174016952515},{"id":"https://openalex.org/keywords/computer-aided-diagnosis","display_name":"Computer-aided diagnosis","score":0.43831461668014526},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42138323187828064},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20407578349113464},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08972316980361938}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.778755784034729},{"id":"https://openalex.org/C2778559731","wikidata":"https://www.wikidata.org/wiki/Q23808793","display_name":"Radiomics","level":2,"score":0.7449933290481567},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7157495021820068},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6747727394104004},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6196832656860352},{"id":"https://openalex.org/C194789388","wikidata":"https://www.wikidata.org/wiki/Q17855283","display_name":"CAD","level":2,"score":0.607661783695221},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5878404974937439},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5129453539848328},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5033299326896667},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4618126153945923},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44655174016952515},{"id":"https://openalex.org/C2779549770","wikidata":"https://www.wikidata.org/wiki/Q1122413","display_name":"Computer-aided diagnosis","level":2,"score":0.43831461668014526},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42138323187828064},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20407578349113464},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08972316980361938},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C199639397","wikidata":"https://www.wikidata.org/wiki/Q1788588","display_name":"Engineering drawing","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2611886","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2611886","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4699999988079071,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1917647701","https://openalex.org/W1972626213","https://openalex.org/W1974636983","https://openalex.org/W2094722563","https://openalex.org/W2096866836","https://openalex.org/W2143858998","https://openalex.org/W2593591744","https://openalex.org/W2618052002","https://openalex.org/W2775577467","https://openalex.org/W2800539391","https://openalex.org/W2801188218","https://openalex.org/W2897120700","https://openalex.org/W2947706148","https://openalex.org/W3021445439","https://openalex.org/W3036908895","https://openalex.org/W3093286749","https://openalex.org/W3166723252","https://openalex.org/W4200409436","https://openalex.org/W6763301180","https://openalex.org/W6784786246","https://openalex.org/W6795767282"],"related_works":["https://openalex.org/W1589419489","https://openalex.org/W4229543669","https://openalex.org/W1783185948","https://openalex.org/W1554029525","https://openalex.org/W2999505641","https://openalex.org/W2401866201","https://openalex.org/W2945629533","https://openalex.org/W615772105","https://openalex.org/W3176438653","https://openalex.org/W1978794434"],"abstract_inverted_index":{"Radiomics":[0],"and":[1,13,56,81,104,142,175,197,204,215,237,240],"deep":[2,230],"transfer":[3,120,231],"learning":[4,121,232],"have":[5,46],"been":[6,48],"attracting":[7],"broad":[8],"research":[9],"interest":[10],"in":[11,27,43,61,74,247],"developing":[12],"optimizing":[14],"CAD":[15,44,88],"schemes":[16,89],"of":[17,38,202],"medical":[18],"images.":[19],"However,":[20],"these":[21,40,58],"two":[22,41,59],"technologies":[23,42,60],"are":[24,90,161],"typically":[25],"applied":[26,100],"different":[28,31],"studies":[29],"using":[30,163,177,186,195,211,229],"image":[32],"datasets.":[33],"Advantages":[34],"or":[35,167],"potential":[36],"limitations":[37],"applying":[39],"applications":[45],"not":[47,219],"well":[49],"investigated.":[50],"This":[51,224],"study":[52,225],"aims":[53],"to":[54,92,101,123,136,145],"compare":[55],"assess":[57],"classifying":[62],"breast":[63,94],"lesions.":[64,86,95],"A":[65],"retrospective":[66],"dataset":[67],"including":[68],"2,778":[69],"digital":[70],"mammograms":[71],"is":[72,99,134,173,184],"assembled":[73],"which":[75],"1,452":[76],"images":[77,83],"depict":[78,84],"malignant":[79],"lesions":[80,103],"1,326":[82],"benign":[85],"Two":[87,192],"developed":[91],"classify":[93],"First,":[96],"one":[97],"scheme":[98,110],"segment":[102],"compute":[105],"radiomics":[106,141,166,196,214,239],"features,":[107],"while":[108],"another":[109],"applies":[111],"a":[112,119,178],"pre-trained":[113],"residual":[114],"net":[115],"architecture":[116],"(ResNet50)":[117],"as":[118],"model":[122,172],"extract":[124],"automated":[125,143,168,198,216,241],"features.":[126,153,169],"Next,":[127],"the":[128,164,212],"same":[129],"principal":[130],"component":[131],"algorithm":[132],"(PCA)":[133],"used":[135],"process":[137],"both":[138],"initially":[139],"computed":[140],"features":[144,199,217,242],"create":[146],"optimal":[147],"feature":[148],"vectors":[149],"by":[150],"eliminating":[151],"redundant":[152],"Then,":[154],"several":[155],"support":[156],"vector":[157],"machine":[158],"(SVM)-based":[159],"classifiers":[160],"built":[162],"optimized":[165],"Each":[170],"SVM":[171,209],"trained":[174,194,210],"tested":[176],"10-fold":[179],"cross-validation":[180],"method.":[181],"Classification":[182],"performance":[183],"evaluated":[185],"area":[187],"under":[188],"ROC":[189],"curve":[190],"(AUC).":[191],"SVMs":[193],"yield":[200,220],"AUC":[201],"0.77&plusmn;0.02":[203],"0.85&plusmn;0.02,":[205],"respectively.":[206],"In":[207],"addition,":[208],"fused":[213],"does":[218],"significantly":[221],"higher":[222,234],"AUC.":[223],"indicates":[226],"that":[227],"(1)":[228],"yields":[233],"classification":[235],"performance,":[236],"(2)":[238],"contain":[243],"highly":[244],"correlated":[245],"information":[246],"lesion":[248],"classification.":[249]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
