{"id":"https://openalex.org/W4200500547","doi":"https://doi.org/10.1109/bibe52308.2021.9635175","title":"Deep Learning and Transfer Learning for Skin Cancer Segmentation and Classification","display_name":"Deep Learning and Transfer Learning for Skin Cancer Segmentation and Classification","publication_year":2021,"publication_date":"2021-10-25","ids":{"openalex":"https://openalex.org/W4200500547","doi":"https://doi.org/10.1109/bibe52308.2021.9635175"},"language":"en","primary_location":{"id":"doi:10.1109/bibe52308.2021.9635175","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibe52308.2021.9635175","pdf_url":null,"source":{"id":"https://openalex.org/S4363608533","display_name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","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/A5100412920","display_name":"Lin Li","orcid":"https://orcid.org/0000-0002-9639-7748"},"institutions":[{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lin Li","raw_affiliation_strings":["Seattle University, Seattle, USA"],"affiliations":[{"raw_affiliation_string":"Seattle University, Seattle, USA","institution_ids":["https://openalex.org/I58610484"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083940567","display_name":"seo wonseok","orcid":"https://orcid.org/0000-0003-0272-2026"},"institutions":[{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wonseok Seo","raw_affiliation_strings":["Seattle University, Seattle, USA"],"affiliations":[{"raw_affiliation_string":"Seattle University, Seattle, USA","institution_ids":["https://openalex.org/I58610484"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100412920"],"corresponding_institution_ids":["https://openalex.org/I58610484"],"apc_list":null,"apc_paid":null,"fwci":2.4837,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.90977444,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9998999834060669,"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9998999834060669,"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/T11306","display_name":"Nonmelanoma Skin Cancer Studies","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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":0.9843999743461609,"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/transfer-of-learning","display_name":"Transfer of learning","score":0.7790597677230835},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7746411561965942},{"id":"https://openalex.org/keywords/skin-cancer","display_name":"Skin cancer","score":0.768839955329895},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7660115361213684},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.71185302734375},{"id":"https://openalex.org/keywords/skin-lesion","display_name":"Skin lesion","score":0.6933732032775879},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6587826609611511},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6344811916351318},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"Multiclass classification","score":0.5810258984565735},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5169290900230408},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4896007180213928},{"id":"https://openalex.org/keywords/lesion","display_name":"Lesion","score":0.44120484590530396},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.42273852229118347},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.38725417852401733},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3697859048843384},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.21979689598083496},{"id":"https://openalex.org/keywords/dermatology","display_name":"Dermatology","score":0.18545114994049072},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.15966731309890747},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.1175769567489624},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06605970859527588}],"concepts":[{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.7790597677230835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7746411561965942},{"id":"https://openalex.org/C2777789703","wikidata":"https://www.wikidata.org/wiki/Q192102","display_name":"Skin cancer","level":3,"score":0.768839955329895},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7660115361213684},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.71185302734375},{"id":"https://openalex.org/C2988168687","wikidata":"https://www.wikidata.org/wiki/Q949302","display_name":"Skin lesion","level":2,"score":0.6933732032775879},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6587826609611511},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6344811916351318},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.5810258984565735},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5169290900230408},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4896007180213928},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.44120484590530396},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.42273852229118347},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.38725417852401733},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3697859048843384},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.21979689598083496},{"id":"https://openalex.org/C16005928","wikidata":"https://www.wikidata.org/wiki/Q171171","display_name":"Dermatology","level":1,"score":0.18545114994049072},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.15966731309890747},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.1175769567489624},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06605970859527588},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibe52308.2021.9635175","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibe52308.2021.9635175","pdf_url":null,"source":{"id":"https://openalex.org/S4363608533","display_name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","score":0.4300000071525574,"display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1668801954","https://openalex.org/W1861492603","https://openalex.org/W1997435908","https://openalex.org/W2005088335","https://openalex.org/W2023204574","https://openalex.org/W2096457735","https://openalex.org/W2164273268","https://openalex.org/W2564782580","https://openalex.org/W2591669284","https://openalex.org/W2607363228","https://openalex.org/W2612806369","https://openalex.org/W2698626679","https://openalex.org/W2765316676","https://openalex.org/W2789357239","https://openalex.org/W2794825826","https://openalex.org/W2891595725","https://openalex.org/W2922703796","https://openalex.org/W2932083555","https://openalex.org/W2946122943","https://openalex.org/W2960571991","https://openalex.org/W2996717109","https://openalex.org/W3012192396","https://openalex.org/W3012614932","https://openalex.org/W3102785203","https://openalex.org/W6637247040","https://openalex.org/W6639102338"],"related_works":["https://openalex.org/W4323355870","https://openalex.org/W3165493969","https://openalex.org/W3185137224","https://openalex.org/W4379260075","https://openalex.org/W4285104889","https://openalex.org/W4310255585","https://openalex.org/W4360585222","https://openalex.org/W4253592280","https://openalex.org/W4245346948","https://openalex.org/W3183540191"],"abstract_inverted_index":{"According":[0],"to":[1,71,120,128],"Skin":[2,147],"Cancer":[3],"Foundation,":[4],"skin":[5,25,63,74,125],"cancer":[6,15,26],"is":[7,27,51,89,97],"by":[8,49,99,155],"far":[9],"the":[10,17,37,101,111,121,141,145,156],"most":[11],"common":[12],"type":[13],"of":[14,24,39,46,62,110,174,181],"in":[16,160],"United":[18],"States":[19],"and":[20,41,53,59,77,118,153,167,176],"worldwide.":[21],"Early":[22],"diagnosis":[23,58],"critical":[28],"because":[29],"proper":[30],"treatment":[31,61],"at":[32],"early":[33],"stages":[34],"can":[35],"increase":[36],"chance":[38],"cure":[40],"recovery.":[42],"However,":[43],"visual":[44],"inspection":[45],"dermoscopic":[47],"images":[48],"dermatologists":[50],"error-prone":[52],"time-consuming.":[54],"To":[55],"ensure":[56],"accurate":[57],"faster":[60],"cancer,":[64],"deep":[65],"learning":[66,96],"techniques":[67],"have":[68,170],"been":[69],"utilized":[70,98],"conduct":[72],"automated":[73],"lesion":[75,92,126,164,168],"segmentation":[76,166],"classification.":[78,135],"In":[79],"this":[80],"paper,":[81],"after":[82],"image":[83],"processing,":[84],"a":[85,130,177],"Mask":[86,113,131],"R-CNN":[87,114,132],"model":[88,115,133],"built":[90],"for":[91,134],"segmentation,":[93],"where":[94],"transfer":[95],"using":[100],"pre-trained":[102],"weights":[103,109],"from":[104,144],"Microsoft":[105],"COCO":[106],"dataset.":[107],"The":[108,163],"trained":[112],"are":[116,138],"saved":[117],"transferred":[119],"next":[122],"task":[123],"-":[124],"classification,":[127],"train":[129],"Our":[136],"experiments":[137],"conducted":[139],"on":[140],"benchmark":[142],"datasets":[143],"International":[146],"Imaging":[148],"Collaboration":[149],"2018":[150],"(ISIC":[151],"2018)":[152],"evaluated":[154],"same":[157],"metrics":[158],"used":[159],"ISIC":[161],"2018.":[162],"boundary":[165],"classification":[169],"achieved":[171],"an":[172],"accuracy":[173,180],"96%":[175],"balanced":[178],"multiclass":[179],"80%,":[182],"respectively.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}