{"id":"https://openalex.org/W3109873398","doi":"https://doi.org/10.1109/iicaiet49801.2020.9257859","title":"The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks","display_name":"The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3109873398","doi":"https://doi.org/10.1109/iicaiet49801.2020.9257859","mag":"3109873398"},"language":"en","primary_location":{"id":"doi:10.1109/iicaiet49801.2020.9257859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iicaiet49801.2020.9257859","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","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/A5041781495","display_name":"Kin Wai Lee","orcid":"https://orcid.org/0000-0002-2612-5771"},"institutions":[{"id":"https://openalex.org/I161371597","display_name":"Universiti of Malaysia Sabah","ror":"https://ror.org/040v70252","country_code":"MY","type":"education","lineage":["https://openalex.org/I161371597"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Kin Wai Lee","raw_affiliation_strings":["Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia","Universiti Malaysia Sabah,Faculty of Engineering,Kota Kinabalu,Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia","institution_ids":["https://openalex.org/I161371597"]},{"raw_affiliation_string":"Universiti Malaysia Sabah,Faculty of Engineering,Kota Kinabalu,Malaysia","institution_ids":["https://openalex.org/I161371597"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076640994","display_name":"Renee Ka Yin Chin","orcid":"https://orcid.org/0000-0002-9992-5593"},"institutions":[{"id":"https://openalex.org/I161371597","display_name":"Universiti of Malaysia Sabah","ror":"https://ror.org/040v70252","country_code":"MY","type":"education","lineage":["https://openalex.org/I161371597"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Renee Ka Yin Chin","raw_affiliation_strings":["Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia","Universiti Malaysia Sabah,Faculty of Engineering,Kota Kinabalu,Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia","institution_ids":["https://openalex.org/I161371597"]},{"raw_affiliation_string":"Universiti Malaysia Sabah,Faculty of Engineering,Kota Kinabalu,Malaysia","institution_ids":["https://openalex.org/I161371597"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I161371597"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9997000098228455,"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","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/T10862","display_name":"AI in cancer detection","score":0.9843000173568726,"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/T11013","display_name":"Skin Protection and Aging","score":0.9794999957084656,"subfield":{"id":"https://openalex.org/subfields/2708","display_name":"Dermatology"},"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7636020183563232},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7308599948883057},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6851311922073364},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5697224140167236},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5508874654769897},{"id":"https://openalex.org/keywords/skin-cancer","display_name":"Skin cancer","score":0.5162601470947266},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5006773471832275},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4940989315509796},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.4153406620025635},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41077059507369995},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3595793545246124},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3505331873893738},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.26187771558761597},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.09158962965011597}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7636020183563232},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7308599948883057},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6851311922073364},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5697224140167236},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5508874654769897},{"id":"https://openalex.org/C2777789703","wikidata":"https://www.wikidata.org/wiki/Q192102","display_name":"Skin cancer","level":3,"score":0.5162601470947266},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5006773471832275},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4940989315509796},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.4153406620025635},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41077059507369995},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3595793545246124},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3505331873893738},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.26187771558761597},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.09158962965011597},{"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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iicaiet49801.2020.9257859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iicaiet49801.2020.9257859","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2163605009","https://openalex.org/W2247923174","https://openalex.org/W2460583509","https://openalex.org/W2618530766","https://openalex.org/W2797286202","https://openalex.org/W2807582799","https://openalex.org/W2900030915","https://openalex.org/W2930514556","https://openalex.org/W2953595166","https://openalex.org/W2954996726","https://openalex.org/W2959246410","https://openalex.org/W2967240347","https://openalex.org/W2971897287"],"related_works":["https://openalex.org/W3108298832","https://openalex.org/W4293226380","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3185137224","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W4210826189"],"abstract_inverted_index":{"Melanoma":[0],"skin":[1,29,130],"cancer":[2,30,131],"has":[3,32,143,158],"been":[4,33],"a":[5],"serious":[6],"threat":[7],"due":[8],"to":[9,54,124,163,199],"its":[10],"high":[11],"fatality.":[12],"For":[13],"this":[14,68,85],"reason,":[15],"early":[16],"detection":[17,31],"and":[18,62,103,117],"treatments":[19],"are":[20,122],"given":[21],"more":[22],"attention":[23],"as":[24,59],"countermeasures.":[25],"In":[26,166],"recent":[27],"years,":[28],"utilizing":[34],"artificial":[35],"intelligence":[36],"techniques,":[37],"specifically":[38],"deep":[39],"convolutional":[40,48],"neural":[41,49],"network.":[42],"However,":[43],"the":[44,47,60,65,71,126,129,159,168,179,189],"performance":[45,127,145],"of":[46,64,73,91,128,153],"network":[50],"is":[51,111,173,183],"highly":[52],"vulnerable":[53],"different":[55,77,89],"data":[56,75,78,92,108],"constraints,":[57],"such":[58],"quality":[61],"quantity":[63],"data.":[66],"Therefore,":[67],"study":[69,138],"explores":[70],"synthetization":[72],"training":[74],"using":[76],"augmentation":[79,93,109,142,149],"methods.":[80,165],"The":[81,133],"work":[82],"presented":[83],"in":[84,136],"paper":[86],"utilizes":[87],"four":[88],"categories":[90],"methods,":[94],"which":[95,187],"include":[96],"geometrical":[97],"transformation,":[98,102],"noise":[99],"addition,":[100,167],"colour":[101],"image":[104,156,176],"mix.":[105,177],"Multiple":[106],"layers":[107,148],"approach":[110],"also":[112],"explored.":[113],"Dataset":[114],"expansion":[115,120,171,182],"strategies":[116],"optimized":[118,180],"dataset":[119,170,181],"scale":[121],"determined":[123,184],"improve":[125],"classification.":[132],"core":[134],"findings":[135],"our":[137],"revealed":[139],"that":[140],"single-layer":[141],"better":[144],"than":[146],"multiple":[147],"approaches,":[150],"where":[151],"region":[152],"interest":[154],"(ROI)":[155],"mix":[157],"highest":[160],"effectiveness":[161],"compared":[162,198],"other":[164],"best":[169,190],"strategy":[172],"random":[174],"ROI":[175],"Finally,":[178],"at":[185,194],"300%,":[186],"yielded":[188],"overall":[191],"test":[192],"accuracy":[193],"82.9%,":[195],"4.6%":[196],"improvement":[197],"unprocessed":[200],"raw":[201],"dataset.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
