{"id":"https://openalex.org/W2564340392","doi":"https://doi.org/10.1109/isocc.2016.7799852","title":"Deep learning application trial to lung cancer diagnosis for medical sensor systems","display_name":"Deep learning application trial to lung cancer diagnosis for medical sensor systems","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2564340392","doi":"https://doi.org/10.1109/isocc.2016.7799852","mag":"2564340392"},"language":"en","primary_location":{"id":"doi:10.1109/isocc.2016.7799852","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isocc.2016.7799852","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International SoC Design Conference (ISOCC)","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/A5001307394","display_name":"Ryota Shimizu","orcid":"https://orcid.org/0000-0001-9600-7255"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryota Shimizu","raw_affiliation_strings":["Faculty of Science and Technology, Keio University, Yokohama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Science and Technology, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080149432","display_name":"Shusuke Yanagawa","orcid":null},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shusuke Yanagawa","raw_affiliation_strings":["Faculty of Science and Technology, Keio University, Yokohama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Science and Technology, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060052139","display_name":"Yasutaka Monde","orcid":null},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yasutaka Monde","raw_affiliation_strings":["Faculty of Science and Technology, Keio University, Yokohama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Science and Technology, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010836886","display_name":"H. Yamagishi","orcid":null},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroki Yamagishi","raw_affiliation_strings":["Faculty of Science and Technology, Keio University, Yokohama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Science and Technology, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068257959","display_name":"Mototsugu Hamada","orcid":"https://orcid.org/0000-0002-0461-4208"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Mototsugu Hamada","raw_affiliation_strings":["Faculty of Science and Technology, Keio University, Yokohama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Science and Technology, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037129157","display_name":"Toru Shimizu","orcid":"https://orcid.org/0000-0003-1385-477X"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toru Shimizu","raw_affiliation_strings":["Faculty of Science and Technology, Keio University, Yokohama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Science and Technology, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073154009","display_name":"Tadahiro Kuroda","orcid":"https://orcid.org/0000-0003-0617-1057"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tadahiro Kuroda","raw_affiliation_strings":["Faculty of Science and Technology, Keio University, Yokohama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Science and Technology, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I203951103"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"191","last_page":"192"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9466999769210815,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6774041652679443},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6764622926712036},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6299035549163818},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.574539065361023},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47714897990226746},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45211541652679443},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.34429144859313965}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6774041652679443},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6764622926712036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6299035549163818},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.574539065361023},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47714897990226746},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45211541652679443},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.34429144859313965}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isocc.2016.7799852","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isocc.2016.7799852","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International SoC Design Conference (ISOCC)","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":0.5199999809265137}],"awards":[],"funders":[{"id":"https://openalex.org/F4320338075","display_name":"Core Research for Evolutional Science and Technology","ror":"https://ror.org/00097mb19"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":1,"referenced_works":["https://openalex.org/W2001502878"],"related_works":["https://openalex.org/W4380075502","https://openalex.org/W4223943233","https://openalex.org/W4312200629","https://openalex.org/W4360585206","https://openalex.org/W4364306694","https://openalex.org/W4380086463","https://openalex.org/W4225161397","https://openalex.org/W3014300295","https://openalex.org/W3164822677","https://openalex.org/W2946016983"],"abstract_inverted_index":{"Personal":[0],"and":[1,25,34,41,57,111,134,143],"easy-to-use":[2],"health":[3],"checking":[4],"system":[5,87,99,113,128,155],"is":[6,18,50,59,140],"an":[7],"attractive":[8],"application":[9],"of":[10,39,54,88,97,153,167,173],"sensor":[11,27],"systems.":[12],"Sensing":[13],"data":[14,31,96],"analysis":[15],"for":[16,61,132,170],"diagnosis":[17,86,136],"important":[19],"as":[20,22],"well":[21],"preparing":[23],"small":[24],"mobile":[26],"nodes":[28],"because":[29,137],"sensing":[30,42,76],"include":[32],"variations":[33],"noises":[35],"reflecting":[36],"individual":[37],"difference":[38],"people":[40],"conditions.":[43],"Deep":[44,48,69,93],"Neural":[45],"Network,":[46],"or":[47,125],"Learning,":[49],"a":[51,85],"well-known":[52],"method":[53],"machine":[55],"learning":[56],"it":[58],"effective":[60],"feature":[62],"extraction":[63],"from":[64,75,102],"pictures.":[65],"Then,":[66],"we":[67,81],"thought":[68],"Learning":[70],"also":[71,163],"can":[72],"extract":[73],"features":[74],"data.":[77],"In":[78],"this":[79,154],"paper,":[80],"tried":[82],"to":[83,146,158,164],"build":[84],"lung":[89,123],"cancer":[90,124],"based":[91],"on":[92],"Learning.":[94],"Input":[95],"the":[98,120],"was":[100],"generated":[101],"human":[103,147],"urine":[104,139],"by":[105],"Gas":[106],"Chromatography":[107],"Mass":[108],"Spectrometer":[109],"(GC-MS)":[110],"our":[112],"achieved":[114],"90%":[115],"accuracy":[116],"in":[117],"judging":[118],"whether":[119],"patient":[121],"had":[122],"not.":[126],"This":[127],"will":[129],"be":[130],"useful":[131],"pre-":[133],"personal":[135],"collecting":[138],"very":[141],"easy":[142],"not":[144,156],"harmful":[145],"body.":[148],"We":[149],"are":[150],"targeting":[151],"installation":[152],"only":[157],"gas":[159],"chromatography":[160],"systems":[161],"but":[162],"some":[165],"combination":[166],"multiple":[168],"sensors":[169],"detecting":[171],"gases":[172],"low":[174],"concentration.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}