{"id":"https://openalex.org/W2994814705","doi":"https://doi.org/10.1109/ipta.2019.8936078","title":"Segmentation-based Deep Learning Fundus Image Analysis","display_name":"Segmentation-based Deep Learning Fundus Image Analysis","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W2994814705","doi":"https://doi.org/10.1109/ipta.2019.8936078","mag":"2994814705"},"language":"en","primary_location":{"id":"doi:10.1109/ipta.2019.8936078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta.2019.8936078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)","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/A5103237161","display_name":"Qian Wu","orcid":"https://orcid.org/0000-0003-3280-5391"},"institutions":[{"id":"https://openalex.org/I52719799","display_name":"Blekinge Institute of Technology","ror":"https://ror.org/0093a8w51","country_code":"SE","type":"education","lineage":["https://openalex.org/I52719799"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Qian Wu","raw_affiliation_strings":["Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden","institution_ids":["https://openalex.org/I52719799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084177418","display_name":"Abbas Cheddad","orcid":"https://orcid.org/0000-0002-4390-411X"},"institutions":[{"id":"https://openalex.org/I52719799","display_name":"Blekinge Institute of Technology","ror":"https://ror.org/0093a8w51","country_code":"SE","type":"education","lineage":["https://openalex.org/I52719799"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Abbas Cheddad","raw_affiliation_strings":["Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden","institution_ids":["https://openalex.org/I52719799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103237161"],"corresponding_institution_ids":["https://openalex.org/I52719799"],"apc_list":null,"apc_paid":null,"fwci":1.6933,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.84755606,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":1.0,"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":1.0,"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/T10250","display_name":"Glaucoma and retinal disorders","score":0.9939000010490417,"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"}},{"id":"https://openalex.org/T10170","display_name":"Retinal Diseases and Treatments","score":0.993399977684021,"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/fundus","display_name":"Fundus (uterus)","score":0.8119560480117798},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7444829344749451},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7310692667961121},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6746754050254822},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.610295832157135},{"id":"https://openalex.org/keywords/diabetic-retinopathy","display_name":"Diabetic retinopathy","score":0.5806009769439697},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5579123497009277},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.46609535813331604},{"id":"https://openalex.org/keywords/retinopathy","display_name":"Retinopathy","score":0.43041759729385376},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.42235758900642395},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4119788110256195},{"id":"https://openalex.org/keywords/retinal","display_name":"Retinal","score":0.4115392565727234},{"id":"https://openalex.org/keywords/ophthalmology","display_name":"Ophthalmology","score":0.29647183418273926},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.18137061595916748}],"concepts":[{"id":"https://openalex.org/C2776391266","wikidata":"https://www.wikidata.org/wiki/Q9612","display_name":"Fundus (uterus)","level":2,"score":0.8119560480117798},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7444829344749451},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7310692667961121},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6746754050254822},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.610295832157135},{"id":"https://openalex.org/C2779829184","wikidata":"https://www.wikidata.org/wiki/Q631361","display_name":"Diabetic retinopathy","level":3,"score":0.5806009769439697},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5579123497009277},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.46609535813331604},{"id":"https://openalex.org/C2778313320","wikidata":"https://www.wikidata.org/wiki/Q550455","display_name":"Retinopathy","level":3,"score":0.43041759729385376},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42235758900642395},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4119788110256195},{"id":"https://openalex.org/C2780827179","wikidata":"https://www.wikidata.org/wiki/Q422001","display_name":"Retinal","level":2,"score":0.4115392565727234},{"id":"https://openalex.org/C118487528","wikidata":"https://www.wikidata.org/wiki/Q161437","display_name":"Ophthalmology","level":1,"score":0.29647183418273926},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.18137061595916748},{"id":"https://openalex.org/C555293320","wikidata":"https://www.wikidata.org/wiki/Q12206","display_name":"Diabetes mellitus","level":2,"score":0.0},{"id":"https://openalex.org/C134018914","wikidata":"https://www.wikidata.org/wiki/Q162606","display_name":"Endocrinology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ipta.2019.8936078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta.2019.8936078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1483010708","https://openalex.org/W1565746575","https://openalex.org/W1972027273","https://openalex.org/W2045227075","https://openalex.org/W2061997187","https://openalex.org/W2089479074","https://openalex.org/W2098485261","https://openalex.org/W2101228824","https://openalex.org/W2103828547","https://openalex.org/W2124521298","https://openalex.org/W2155063211","https://openalex.org/W2160028189","https://openalex.org/W2164848143","https://openalex.org/W2441806983","https://openalex.org/W2554946065","https://openalex.org/W2620760558","https://openalex.org/W2801013643","https://openalex.org/W2904911410","https://openalex.org/W2906759475","https://openalex.org/W2921216331","https://openalex.org/W2922410398","https://openalex.org/W2952436003","https://openalex.org/W3134431802","https://openalex.org/W3183227663","https://openalex.org/W4235787350","https://openalex.org/W4245055982","https://openalex.org/W4300633279","https://openalex.org/W6798651343"],"related_works":["https://openalex.org/W1980571360","https://openalex.org/W186124986","https://openalex.org/W3167510834","https://openalex.org/W3176448898","https://openalex.org/W4379115808","https://openalex.org/W3207986206","https://openalex.org/W2429379476","https://openalex.org/W1979866946","https://openalex.org/W2584074590","https://openalex.org/W4226141369"],"abstract_inverted_index":{"Diabetic":[0],"retinopathy":[1,86,106],"is":[2,19,70,118],"the":[3,20,24,27,45,55,59,85,91,100,115,125,128,132,149,166,179,203],"most":[4],"common":[5],"cause":[6],"of":[7,10,14,26,47,58,62,84,103,151,181],"new":[8],"cases":[9],"blindness":[11],"in":[12,81,94],"people":[13],"working":[15],"age.":[16],"Early":[17],"diagnosis":[18],"key":[21],"to":[22,71,89],"slowing":[23],"progression":[25],"disease,":[28,87],"thus":[29],"preventing":[30],"blindness.":[31],"Retinal":[32],"fundus":[33,95,116,153,205],"images":[34,64,96],"form":[35],"an":[36],"important":[37],"basis":[38],"for":[39,105,184],"judging":[40],"these":[41],"retinal":[42,63],"diseases.":[43],"To":[44,108],"best":[46,92],"our":[48],"knowledge,":[49],"no":[50],"prior":[51],"studies":[52],"have":[53],"scrutinized":[54],"predictive":[56,200],"power":[57,102,201],"different":[60,122],"compositions":[61],"using":[65,209],"deep":[66,163],"learning.":[67],"This":[68],"paper":[69],"investigate":[72],"whether":[73],"there":[74],"exists":[75],"specific":[76],"region":[77,93],"that":[78,97],"could":[79],"assist":[80],"better":[82],"prediction":[83,101],"meaning":[88],"find":[90],"can":[98],"boost":[99],"models":[104,169],"classification.":[107],"this":[109],"end,":[110],"with":[111,165],"image":[112,117,206],"segmentation":[113],"techniques,":[114],"divided":[119],"into":[120],"three":[121],"segments,":[123],"namely,":[124],"optic":[126,141],"disc,":[127],"blood":[129,138],"vessels,":[130],"and":[131,140],"other":[133,136,195],"regions":[134,144],"(regions":[135],"than":[137,202],"vessels":[139],"disk).":[142],"These":[143],"are":[145,175,192],"then":[146],"contrasted":[147],"against":[148],"performance":[150],"original":[152,204],"images.":[154],"The":[155,194],"convolutional":[156],"neural":[157],"network":[158],"as":[159,161],"well":[160],"transfer":[162],"learning":[164,189],"state-of-the-art":[167],"pre-trained":[168],"(i.e.,":[170],"AlexNet,":[171],"GoogleNet,":[172],"Resnet50,":[173],"VGG19)":[174],"deployed.":[176],"We":[177],"report":[178],"average":[180],"ten":[182],"runs":[183],"each":[185],"model.":[186],"Different":[187],"machine":[188],"evaluation":[190],"metrics":[191],"used.":[193],"regions'":[196],"segment":[197],"reveals":[198],"more":[199],"especially":[207],"when":[208],"AlexNet/Resnet50.":[210]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}