{"id":"https://openalex.org/W2794169953","doi":"https://doi.org/10.1109/access.2018.2817593","title":"Breast Cancer Classification Based on Fully-Connected Layer First Convolutional Neural Networks","display_name":"Breast Cancer Classification Based on Fully-Connected Layer First Convolutional Neural Networks","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2794169953","doi":"https://doi.org/10.1109/access.2018.2817593","mag":"2794169953"},"language":"en","primary_location":{"id":"doi:10.1109/access.2018.2817593","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2817593","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2018.2817593","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059901583","display_name":"Kui Liu","orcid":"https://orcid.org/0000-0003-2715-5304"},"institutions":[{"id":"https://openalex.org/I77631543","display_name":"Wuxi Institute of Technology","ror":"https://ror.org/00ab95029","country_code":"CN","type":"education","lineage":["https://openalex.org/I77631543"]},{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kui Liu","raw_affiliation_strings":["Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing, China","Wuxi BUPT Sensory Technology and Industry Institute Co. Ltd., Wuxi, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Wuxi BUPT Sensory Technology and Industry Institute Co. Ltd., Wuxi, China","institution_ids":["https://openalex.org/I77631543"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085386009","display_name":"Guixia Kang","orcid":"https://orcid.org/0000-0002-4039-4505"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]},{"id":"https://openalex.org/I77631543","display_name":"Wuxi Institute of Technology","ror":"https://ror.org/00ab95029","country_code":"CN","type":"education","lineage":["https://openalex.org/I77631543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guixia Kang","raw_affiliation_strings":["Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing, China","Wuxi BUPT Sensory Technology and Industry Institute Co. Ltd., Wuxi, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Wuxi BUPT Sensory Technology and Industry Institute Co. Ltd., Wuxi, China","institution_ids":["https://openalex.org/I77631543"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021624732","display_name":"Ningbo Zhang","orcid":"https://orcid.org/0000-0002-7007-9741"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]},{"id":"https://openalex.org/I77631543","display_name":"Wuxi Institute of Technology","ror":"https://ror.org/00ab95029","country_code":"CN","type":"education","lineage":["https://openalex.org/I77631543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ningbo Zhang","raw_affiliation_strings":["Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing, China","Wuxi BUPT Sensory Technology and Industry Institute Co. Ltd., Wuxi, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Wuxi BUPT Sensory Technology and Industry Institute Co. Ltd., Wuxi, China","institution_ids":["https://openalex.org/I77631543"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044883728","display_name":"Beibei Hou","orcid":"https://orcid.org/0000-0002-1003-9449"},"institutions":[{"id":"https://openalex.org/I77631543","display_name":"Wuxi Institute of Technology","ror":"https://ror.org/00ab95029","country_code":"CN","type":"education","lineage":["https://openalex.org/I77631543"]},{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Beibei Hou","raw_affiliation_strings":["Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing, China","Wuxi BUPT Sensory Technology and Industry Institute Co. Ltd., Wuxi, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Wuxi BUPT Sensory Technology and Industry Institute Co. Ltd., Wuxi, China","institution_ids":["https://openalex.org/I77631543"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5059901583"],"corresponding_institution_ids":["https://openalex.org/I139759216","https://openalex.org/I77631543"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":7.0944,"has_fulltext":false,"cited_by_count":108,"citation_normalized_percentile":{"value":0.9751932,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"6","issue":null,"first_page":"23722","last_page":"23732"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998999834060669,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9812999963760376,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9789000153541565,"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/computer-science","display_name":"Computer science","score":0.8499687314033508},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8408872485160828},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6430456638336182},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.619191586971283},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.552614688873291},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.48359817266464233},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.4830935001373291},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4439142346382141},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.43261998891830444},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42702123522758484},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4179800748825073},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3523830771446228},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2850506007671356}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8499687314033508},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8408872485160828},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6430456638336182},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.619191586971283},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.552614688873291},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.48359817266464233},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4830935001373291},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4439142346382141},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.43261998891830444},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42702123522758484},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4179800748825073},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3523830771446228},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2850506007671356},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2018.2817593","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2817593","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:be6b4893f8684a61812b2aedd7601492","is_oa":true,"landing_page_url":"https://doaj.org/article/be6b4893f8684a61812b2aedd7601492","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 6, Pp 23722-23732 (2018)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2018.2817593","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2817593","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.5699999928474426,"display_name":"Good health and well-being"}],"awards":[{"id":"https://openalex.org/G5522554564","display_name":null,"funder_award_id":"61471064","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1543715688","https://openalex.org/W1564809434","https://openalex.org/W1596717185","https://openalex.org/W1678356000","https://openalex.org/W1833977909","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1965927354","https://openalex.org/W1968320709","https://openalex.org/W1969557815","https://openalex.org/W2008056655","https://openalex.org/W2065281378","https://openalex.org/W2075526913","https://openalex.org/W2077487563","https://openalex.org/W2098729027","https://openalex.org/W2101234009","https://openalex.org/W2109638028","https://openalex.org/W2117539524","https://openalex.org/W2124965119","https://openalex.org/W2141125852","https://openalex.org/W2155487617","https://openalex.org/W2167389327","https://openalex.org/W2169171650","https://openalex.org/W2173314530","https://openalex.org/W2183341477","https://openalex.org/W2214125478","https://openalex.org/W2295598076","https://openalex.org/W2911964244","https://openalex.org/W3165241807","https://openalex.org/W4236531604","https://openalex.org/W4248437541","https://openalex.org/W6620707391","https://openalex.org/W6633844846","https://openalex.org/W6669921165","https://openalex.org/W6675354045","https://openalex.org/W6796017329"],"related_works":["https://openalex.org/W3183901164","https://openalex.org/W2951211570","https://openalex.org/W3135818718","https://openalex.org/W4290188444","https://openalex.org/W2253429366","https://openalex.org/W3127975138","https://openalex.org/W2911497689","https://openalex.org/W2952813363","https://openalex.org/W2963346891","https://openalex.org/W4360783045"],"abstract_inverted_index":{"Both":[0],"Wisconsin":[1,9],"diagnostic":[2],"breast":[3,10],"cancer":[4,11],"(WDBC)":[5],"database":[6,12],"and":[7,147,162,165,190,209,216,225],"the":[8,31,37,64,89,104,110,116,160,167,178,241],"(WBCD)":[13],"are":[14,107,237],"structured":[15,78,93],"datasets":[16,38,164,236],"described":[17],"by":[18,152,169],"cytological":[19],"features.":[20],"In":[21,80,134],"this":[22],"paper,":[23],"we":[24,95,141],"were":[25],"seeking":[26],"to":[27,60,82,87,125,136,159,240],"identify":[28],"ways":[29],"improve":[30,88],"classification":[32,90,184],"performance":[33,76,91,185],"for":[34,49,53,77,92,193,214,230,234],"each":[35],"of":[36,66,85,145,203,207,212,219,223,228,243],"based":[39],"on":[40],"convolutional":[41,112],"neural":[42],"networks":[43],"(CNN).":[44],"However,":[45],"CNN":[46,71,86,100,192],"is":[47],"designed":[48],"unstructured":[50],"data,":[51,55,94],"especially":[52],"image":[54,67],"which":[56,103],"has":[57],"been":[58],"proven":[59],"be":[61],"successful":[62],"in":[63,102],"field":[65],"recognition.":[68],"A":[69],"typical":[70],"may":[72],"not":[73],"keep":[74],"its":[75],"data.":[79],"order":[81,135],"take":[83],"advantage":[84],"proposed":[96],"fully-connected":[97,105,117],"layer":[98,118],"first":[99,111],"(FCLF-CNN),":[101],"layers":[106],"embedded":[108],"before":[109],"layer.":[113],"We":[114,155],"used":[115],"as":[119],"an":[120,123,149,201,217],"encoder":[121],"or":[122],"approximator":[124],"transfer":[126],"raw":[127],"samples":[128],"into":[129],"representations":[130],"with":[131],"more":[132],"locality.":[133],"get":[137],"a":[138,170,182,205,210,221,226],"better":[139,183],"performance,":[140],"trained":[142],"four":[143],"kinds":[144],"FCLF-CNNs":[146],"made":[148],"ensemble":[150,197],"FCLF-CNN":[151,179,198],"integrating":[153],"them.":[154],"then":[156],"applied":[157],"it":[158],"WDBC":[161],"WBCD":[163],"obtained":[166],"results":[168,175,233,242],"fivefold":[171],"cross":[172],"validation.":[173],"The":[174,196,232],"showed":[176],"that":[177],"can":[180,199],"achieve":[181,200],"than":[186],"pure":[187,191],"multi-layer":[188],"perceptrons":[189],"both":[194,235],"datasets.":[195],"accuracy":[202,218],"99.28%,":[204],"sensitivity":[206,222],"98.65%,":[208],"specificity":[211,227],"99.57%":[213],"WDBC,":[215],"98.71%,":[220],"97.60%,":[224],"99.43%":[229],"WBCD.":[231],"competitive":[238],"compared":[239],"other":[244],"research.":[245]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":20},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":1}],"updated_date":"2026-03-06T13:50:29.536080","created_date":"2025-10-10T00:00:00"}