{"id":"https://openalex.org/W3196345673","doi":"https://doi.org/10.1145/3468891.3468902","title":"Pigmented Skin Lesions Image Classification Based on Residual Network","display_name":"Pigmented Skin Lesions Image Classification Based on Residual Network","publication_year":2021,"publication_date":"2021-04-23","ids":{"openalex":"https://openalex.org/W3196345673","doi":"https://doi.org/10.1145/3468891.3468902","mag":"3196345673"},"language":"en","primary_location":{"id":"doi:10.1145/3468891.3468902","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3468891.3468902","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 6th International Conference on Machine Learning Technologies","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/A5060953751","display_name":"Xiangyang Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I52158045","display_name":"China Agricultural University","ror":"https://ror.org/04v3ywz14","country_code":"CN","type":"education","lineage":["https://openalex.org/I52158045"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiangyang Yin","raw_affiliation_strings":["China Agricultural University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Agricultural University, China","institution_ids":["https://openalex.org/I52158045"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5060953751"],"corresponding_institution_ids":["https://openalex.org/I52158045"],"apc_list":null,"apc_paid":null,"fwci":1.4789,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.85518474,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9973000288009644,"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":0.9973000288009644,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9787999987602234,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7043887376785278},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6032728552818298},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5665799379348755},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.48963743448257446},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4791414141654968},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4352565407752991},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3930032253265381},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.09458225965499878}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7043887376785278},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6032728552818298},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5665799379348755},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.48963743448257446},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4791414141654968},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4352565407752991},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3930032253265381},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.09458225965499878}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3468891.3468902","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3468891.3468902","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 6th International Conference on Machine Learning Technologies","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W1985187105","https://openalex.org/W2000853273","https://openalex.org/W2001378671","https://openalex.org/W2005166776","https://openalex.org/W2020900027","https://openalex.org/W2038781708","https://openalex.org/W2044097773","https://openalex.org/W2062227835","https://openalex.org/W2064491113","https://openalex.org/W2119549095","https://openalex.org/W2141453076","https://openalex.org/W2152950860","https://openalex.org/W2158941848","https://openalex.org/W2194775991","https://openalex.org/W2559785631","https://openalex.org/W2580596898","https://openalex.org/W2995942064","https://openalex.org/W3102785203","https://openalex.org/W4233637797","https://openalex.org/W4238498873","https://openalex.org/W6644441217","https://openalex.org/W6682889407","https://openalex.org/W6712165758"],"related_works":["https://openalex.org/W2560215812","https://openalex.org/W2949601986","https://openalex.org/W2788972299","https://openalex.org/W2521347458","https://openalex.org/W2498789492","https://openalex.org/W2729981612","https://openalex.org/W2925692864","https://openalex.org/W4233449973","https://openalex.org/W4391013256","https://openalex.org/W2768526084"],"abstract_inverted_index":{"Pigmented":[0],"Skin":[1],"lesions":[2,32,58],"image":[3,23,33,59],"analysis":[4],"is":[5,17,39,91],"an":[6,18,66,92],"essential":[7],"diagnostic":[8],"tool":[9],"for":[10,30],"skin":[11,31,57],"cancer.":[12],"Since":[13],"convolutional":[14,27],"neural":[15,28],"network":[16,29],"important":[19],"method":[20],"to":[21,35,95],"solve":[22],"classification":[24,34],"problem,":[25],"using":[26,88],"assist":[36],"the":[37],"diagnosis":[38],"practically":[40],"significant.":[41],"This":[42],"paper":[43],"builds":[44],"and":[45],"compares":[46],"two":[47],"50-layer":[48],"ResNet":[49,72],"performances":[50],"based":[51,73],"on":[52,74],"different":[53],"building":[54,76],"blocks":[55],"in":[56],"classification.":[60],"Both":[61],"models":[62],"performed":[63],"similarly":[64],"with":[65],"accuracy":[67],"of":[68],"about":[69,82],"75%.":[70],"Nevertheless,":[71],"bottleneck":[75,89],"block":[77,90],"improves":[78],"training":[79,98],"speed":[80],"by":[81],"51%":[83],"without":[84],"sacrificing":[85],"accuracy.":[86],"Therefore,":[87],"effective":[93],"way":[94],"improve":[96],"model's":[97],"speed.":[99]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
