{"id":"https://openalex.org/W2790383489","doi":"https://doi.org/10.1109/cisp-bmei.2017.8302240","title":"A classification model for the prostate cancer based on deep learning","display_name":"A classification model for the prostate cancer based on deep learning","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2790383489","doi":"https://doi.org/10.1109/cisp-bmei.2017.8302240","mag":"2790383489"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei.2017.8302240","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2017.8302240","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5100414092","display_name":"Ying Liu","orcid":"https://orcid.org/0000-0001-9319-5940"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ying Liu","raw_affiliation_strings":["School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I148128674"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017479414","display_name":"AN Xiao-mei","orcid":null},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaomei An","raw_affiliation_strings":["School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I148128674"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100414092"],"corresponding_institution_ids":["https://openalex.org/I148128674"],"apc_list":null,"apc_paid":null,"fwci":0.5461,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.76949126,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9851999878883362,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9851999878883362,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9670000076293945,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6958931684494019},{"id":"https://openalex.org/keywords/grading","display_name":"Grading (engineering)","score":0.6515153050422668},{"id":"https://openalex.org/keywords/prostate-cancer","display_name":"Prostate cancer","score":0.6471074819564819},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.6233453154563904},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5793086290359497},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5663729310035706},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5599854588508606},{"id":"https://openalex.org/keywords/prostate","display_name":"Prostate","score":0.5146209001541138},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4804793894290924},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.45547816157341003},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4532304108142853},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.37681302428245544},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.29178386926651},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.08831655979156494}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6958931684494019},{"id":"https://openalex.org/C2777286243","wikidata":"https://www.wikidata.org/wiki/Q5591926","display_name":"Grading (engineering)","level":2,"score":0.6515153050422668},{"id":"https://openalex.org/C2780192828","wikidata":"https://www.wikidata.org/wiki/Q181257","display_name":"Prostate cancer","level":3,"score":0.6471074819564819},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.6233453154563904},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5793086290359497},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5663729310035706},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5599854588508606},{"id":"https://openalex.org/C2776235491","wikidata":"https://www.wikidata.org/wiki/Q9625","display_name":"Prostate","level":3,"score":0.5146209001541138},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4804793894290924},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.45547816157341003},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4532304108142853},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.37681302428245544},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.29178386926651},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.08831655979156494},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei.2017.8302240","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2017.8302240","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1980977406","https://openalex.org/W1982668309","https://openalex.org/W1988777729","https://openalex.org/W2011370294","https://openalex.org/W2058307353","https://openalex.org/W2097117768","https://openalex.org/W2100351660","https://openalex.org/W2100495367","https://openalex.org/W2101926813","https://openalex.org/W2112796928","https://openalex.org/W2147800946","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2351736210","https://openalex.org/W2526050071","https://openalex.org/W2562404687","https://openalex.org/W2571084581","https://openalex.org/W2579874689","https://openalex.org/W2614256707","https://openalex.org/W2753867498","https://openalex.org/W2949650786","https://openalex.org/W3004243356","https://openalex.org/W3103753223","https://openalex.org/W6637373629","https://openalex.org/W6645652798","https://openalex.org/W6674914833","https://openalex.org/W6684191040","https://openalex.org/W6705753509","https://openalex.org/W6732481898","https://openalex.org/W6773413034"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W3024479225","https://openalex.org/W4323287533","https://openalex.org/W3171371563","https://openalex.org/W2995680918"],"abstract_inverted_index":{"Regarded":[0],"as":[1,72],"one":[2],"of":[3,17,31,36,40,63,87,146,157,176,185,192,238],"the":[4,7,15,24,38,59,73,105,117,158,165,173,182,234],"common":[5],"cancers,":[6],"prostate":[8,41,108,239],"cancer":[9,42,109,240,243],"is":[10,179,188],"a":[11,85,94],"main":[12],"reason":[13],"harming":[14],"health":[16],"senile":[18],"men,":[19],"especially":[20],"in":[21,135,142],"Europe":[22],"and":[23,34,46,51,90,101,116,164,181,196,215,236,241],"United":[25],"States.":[26],"In":[27,58],"China,":[28],"with":[29,84,97],"increasing":[30],"living":[32],"standards":[33],"aging":[35],"populations,":[37],"incidence":[39],"has":[43,69,80,202],"an":[44,122],"upward":[45],"younger":[47],"tendency.":[48],"Early":[49],"detection":[50],"early":[52],"treatment":[53],"are":[54,212],"helpful":[55],"to":[56,233],"patients.":[57],"imaging":[60,67,152],"diagnosis":[61,91,106,130],"methods":[62],"prostate,":[64],"magnetic":[65,150],"resonance":[66,151],"(MRI)":[68],"been":[70,203],"recognized":[71],"most":[74],"effective":[75],"way.":[76],"Whereas":[77],"MRI":[78],"image":[79,123],"several":[81],"specialized":[82],"configurations":[83],"lot":[86],"medical":[88],"information,":[89],"results":[92],"have":[93],"strong":[95],"relationship":[96],"doctor's":[98],"professional":[99],"skill":[100],"experience,":[102],"which":[103,126],"makes":[104],"for":[107,162,168,209],"more":[110],"difficult.":[111],"Based":[112],"on":[113],"deep":[114,226],"learning":[115,227],"Convolutional":[118],"Neural":[119],"Networks":[120],"(CNN),":[121],"classification":[124,131],"model":[125,201],"can":[127,229],"provide":[128],"some":[129,216],"reference":[132],"was":[133],"proposed":[134],"this":[136,143,200,225],"paper.":[137],"The":[138,190,206],"data":[139],"sets":[140],"used":[141,161],"paper":[144],"consisted":[145],"10056":[147],"diffusion":[148],"weighted":[149],"(DWI)":[153],"images.":[154],"Three":[155],"quarters":[156],"images":[159,167,211],"were":[160],"training":[163,177],"rest":[166],"testing.":[169],"Experiments":[170],"show":[171,198],"that":[172,199],"accuracy":[174,183,194,207],"rate":[175,184,195,208],"set":[178,187],"80.1539%,":[180],"testing":[186,193],"78.1538%.":[189],"curves":[191],"loss":[197],"trained":[204],"steadily.":[205],"single":[210],"above":[213],"64.91%,":[214],"may":[217],"reach":[218],"99.99%.":[219],"With":[220],"certain":[221],"clinical":[222],"application":[223],"value,":[224],"method":[228],"be":[230],"widely":[231],"applied":[232],"grading":[235],"staging":[237],"other":[242],"tasks.":[244]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}