{"id":"https://openalex.org/W2890575186","doi":"https://doi.org/10.1109/icassp.2018.8462089","title":"Combining Multiple Deep Features for Glaucoma Classification","display_name":"Combining Multiple Deep Features for Glaucoma Classification","publication_year":2018,"publication_date":"2018-04-01","ids":{"openalex":"https://openalex.org/W2890575186","doi":"https://doi.org/10.1109/icassp.2018.8462089","mag":"2890575186"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2018.8462089","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8462089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5102781484","display_name":"Annan Li","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Annan Li","raw_affiliation_strings":["Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115589096","display_name":"Yunhong Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunhong Wang","raw_affiliation_strings":["Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044959745","display_name":"Jun Cheng","orcid":"https://orcid.org/0000-0003-1786-6188"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210165339","display_name":"Ningbo Institute of Industrial Technology","ror":"https://ror.org/05nqg3g04","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165339"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Cheng","raw_affiliation_strings":["Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Zhengjiang, China"],"affiliations":[{"raw_affiliation_string":"Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Zhengjiang, China","institution_ids":["https://openalex.org/I4210165339","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100325209","display_name":"Jiang Liu","orcid":"https://orcid.org/0000-0001-6281-6505"},"institutions":[{"id":"https://openalex.org/I4210165339","display_name":"Ningbo Institute of Industrial Technology","ror":"https://ror.org/05nqg3g04","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165339"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiang Liu","raw_affiliation_strings":["Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Zhengjiang, China"],"affiliations":[{"raw_affiliation_string":"Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Zhengjiang, China","institution_ids":["https://openalex.org/I4210165339","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102781484"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":2.1217,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.87901985,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"985","last_page":"989"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T11438","display_name":"Retinal Imaging and Analysis","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9975000023841858,"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/T10250","display_name":"Glaucoma and retinal disorders","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/2731","display_name":"Ophthalmology"},"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":0.8153567314147949},{"id":"https://openalex.org/keywords/glaucoma","display_name":"Glaucoma","score":0.8135542869567871},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7710399031639099},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7226207256317139},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5764939785003662},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.5494397282600403},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5438507199287415},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5398634076118469},{"id":"https://openalex.org/keywords/blindness","display_name":"Blindness","score":0.5371872186660767},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5140382051467896},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4711426794528961},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4621148407459259},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3817628026008606},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.342878520488739},{"id":"https://openalex.org/keywords/optometry","display_name":"Optometry","score":0.14036110043525696},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.10166418552398682},{"id":"https://openalex.org/keywords/ophthalmology","display_name":"Ophthalmology","score":0.07846063375473022}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8153567314147949},{"id":"https://openalex.org/C2778527774","wikidata":"https://www.wikidata.org/wiki/Q159701","display_name":"Glaucoma","level":2,"score":0.8135542869567871},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7710399031639099},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7226207256317139},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5764939785003662},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.5494397282600403},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5438507199287415},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5398634076118469},{"id":"https://openalex.org/C2780929884","wikidata":"https://www.wikidata.org/wiki/Q737460","display_name":"Blindness","level":2,"score":0.5371872186660767},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5140382051467896},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4711426794528961},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4621148407459259},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3817628026008606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.342878520488739},{"id":"https://openalex.org/C119767625","wikidata":"https://www.wikidata.org/wiki/Q618211","display_name":"Optometry","level":1,"score":0.14036110043525696},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.10166418552398682},{"id":"https://openalex.org/C118487528","wikidata":"https://www.wikidata.org/wiki/Q161437","display_name":"Ophthalmology","level":1,"score":0.07846063375473022}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2018.8462089","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8462089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":19,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1904432309","https://openalex.org/W2009263890","https://openalex.org/W2016803658","https://openalex.org/W2049996624","https://openalex.org/W2056039205","https://openalex.org/W2081178133","https://openalex.org/W2097117768","https://openalex.org/W2140630265","https://openalex.org/W2153635508","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2402050431","https://openalex.org/W2755129795","https://openalex.org/W6639940742","https://openalex.org/W6663219967","https://openalex.org/W6664704990","https://openalex.org/W6684191040","https://openalex.org/W6687483927"],"related_works":["https://openalex.org/W4385649027","https://openalex.org/W2082087664","https://openalex.org/W2070104888","https://openalex.org/W4400094315","https://openalex.org/W4285351923","https://openalex.org/W4389542577","https://openalex.org/W2885840481","https://openalex.org/W4240661191","https://openalex.org/W1998930228","https://openalex.org/W2038649333"],"abstract_inverted_index":{"Glaucoma":[0],"is":[1,11,26,89,126],"one":[2],"of":[3,7,109],"the":[4,31,93,104,114,127],"leading":[5],"cause":[6],"blindness.":[8],"Although":[9],"there":[10],"still":[12],"no":[13],"cure,":[14],"early":[15],"detection":[16],"can":[17,60],"prevent":[18],"serious":[19],"vision":[20],"loss.":[21],"Therefore":[22],"automated":[23],"glaucoma":[24,75,133],"detection/classification":[25],"an":[27,101],"important":[28],"issue.":[29],"In":[30,68],"past":[32],"decade,":[33],"segmentation":[34],"based":[35,40,55],"approach":[36,129],"such":[37],"as":[38],"those":[39],"on":[41,92,118],"cup-to-disc-ratio":[42],"are":[43],"popular,":[44],"but":[45],"single":[46],"indicator":[47],"limit":[48],"its":[49],"performance.":[50,67],"Recently,":[51],"convolutional":[52,84],"neural":[53,85],"network":[54],"image":[56,63],"classification":[57,76],"approaches":[58],"that":[59],"use":[61],"more":[62],"cues":[64],"achieve":[65],"good":[66],"this":[69],"paper,":[70],"we":[71],"propose":[72],"a":[73],"new":[74],"by":[77,82,117],"combining":[78],"multiple":[79],"features":[80],"extracted":[81],"different":[83],"networks.":[86],"Its":[87],"effectiveness":[88],"clearly":[90],"demonstrated":[91],"publicly":[94],"available":[95],"Origa":[96],"[1]":[97],"dataset.":[98],"It":[99],"achieves":[100],"area":[102],"under":[103],"receiver":[105],"operating":[106],"characteristic":[107],"curve":[108],"0.8483,":[110],"which":[111],"better":[112],"than":[113],"0.838":[115],"given":[116],"manual":[119],"marked":[120],"cup-to-disc-ratio.":[121],"To":[122],"our":[123],"knowledge,":[124],"it":[125],"first":[128],"surpass":[130],"human":[131],"in":[132],"classification.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}