{"id":"https://openalex.org/W4206936205","doi":"https://doi.org/10.1145/3470976","title":"A Convolutional Neural Network Model Using Weighted Loss Function to Detect Diabetic Retinopathy","display_name":"A Convolutional Neural Network Model Using Weighted Loss Function to Detect Diabetic Retinopathy","publication_year":2022,"publication_date":"2022-01-25","ids":{"openalex":"https://openalex.org/W4206936205","doi":"https://doi.org/10.1145/3470976"},"language":"en","primary_location":{"id":"doi:10.1145/3470976","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3470976","pdf_url":null,"source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","raw_type":"journal-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/A5045359397","display_name":"Mehedi Masud","orcid":"https://orcid.org/0000-0001-6019-7245"},"institutions":[{"id":"https://openalex.org/I179331831","display_name":"Taif University","ror":"https://ror.org/014g1a453","country_code":"SA","type":"education","lineage":["https://openalex.org/I179331831"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Mehedi Masud","raw_affiliation_strings":["Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia"],"raw_orcid":"https://orcid.org/0000-0001-6019-7245","affiliations":[{"raw_affiliation_string":"Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia","institution_ids":["https://openalex.org/I179331831"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072017302","display_name":"Mohammed F. Alhamid","orcid":"https://orcid.org/0000-0003-1392-1426"},"institutions":[{"id":"https://openalex.org/I28022161","display_name":"King Saud University","ror":"https://ror.org/02f81g417","country_code":"SA","type":"education","lineage":["https://openalex.org/I28022161"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Mohammed F. Alhamid","raw_affiliation_strings":["Research Chair of Smart Technologies, College of Computer andInformation Sciences, King Saud University, Riyadh, Saudi Arabia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Chair of Smart Technologies, College of Computer andInformation Sciences, King Saud University, Riyadh, Saudi Arabia","institution_ids":["https://openalex.org/I28022161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100343712","display_name":"Yin Zhang\u22c6","orcid":"https://orcid.org/0000-0002-1772-0763"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yin Zhang","raw_affiliation_strings":["University of Electronic Science and Technology of China, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.5869,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.89357224,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"18","issue":"1s","first_page":"1","last_page":"16"},"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9905999898910522,"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":0.7358159422874451},{"id":"https://openalex.org/keywords/diabetic-retinopathy","display_name":"Diabetic retinopathy","score":0.7008749842643738},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6807212829589844},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.62565678358078},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6206544041633606},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5950957536697388},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.5306284427642822},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44110560417175293},{"id":"https://openalex.org/keywords/glaucoma","display_name":"Glaucoma","score":0.42617931962013245},{"id":"https://openalex.org/keywords/retinopathy","display_name":"Retinopathy","score":0.41245734691619873},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3547886312007904},{"id":"https://openalex.org/keywords/diabetes-mellitus","display_name":"Diabetes mellitus","score":0.2940734326839447},{"id":"https://openalex.org/keywords/ophthalmology","display_name":"Ophthalmology","score":0.15123873949050903}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7358159422874451},{"id":"https://openalex.org/C2779829184","wikidata":"https://www.wikidata.org/wiki/Q631361","display_name":"Diabetic retinopathy","level":3,"score":0.7008749842643738},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6807212829589844},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.62565678358078},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6206544041633606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5950957536697388},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.5306284427642822},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44110560417175293},{"id":"https://openalex.org/C2778527774","wikidata":"https://www.wikidata.org/wiki/Q159701","display_name":"Glaucoma","level":2,"score":0.42617931962013245},{"id":"https://openalex.org/C2778313320","wikidata":"https://www.wikidata.org/wiki/Q550455","display_name":"Retinopathy","level":3,"score":0.41245734691619873},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3547886312007904},{"id":"https://openalex.org/C555293320","wikidata":"https://www.wikidata.org/wiki/Q12206","display_name":"Diabetes mellitus","level":2,"score":0.2940734326839447},{"id":"https://openalex.org/C118487528","wikidata":"https://www.wikidata.org/wiki/Q161437","display_name":"Ophthalmology","level":1,"score":0.15123873949050903},{"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:10.1145/3470976","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3470976","pdf_url":null,"source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1972376260","https://openalex.org/W1983059581","https://openalex.org/W1987163675","https://openalex.org/W2051942570","https://openalex.org/W2086824809","https://openalex.org/W2089698565","https://openalex.org/W2112796928","https://openalex.org/W2117539524","https://openalex.org/W2132309556","https://openalex.org/W2141470244","https://openalex.org/W2151349744","https://openalex.org/W2158518042","https://openalex.org/W2159764117","https://openalex.org/W2187089797","https://openalex.org/W2287206980","https://openalex.org/W2346062110","https://openalex.org/W2467570466","https://openalex.org/W2523246573","https://openalex.org/W2524898498","https://openalex.org/W2525266674","https://openalex.org/W2618530766","https://openalex.org/W2733384076","https://openalex.org/W2764302726","https://openalex.org/W2767040741","https://openalex.org/W2885001392","https://openalex.org/W2905473569","https://openalex.org/W2910379596","https://openalex.org/W2915149867","https://openalex.org/W2918087949","https://openalex.org/W2919115771","https://openalex.org/W2938769011","https://openalex.org/W2953391430","https://openalex.org/W2953602099","https://openalex.org/W2963446712","https://openalex.org/W3010500903","https://openalex.org/W3015256491","https://openalex.org/W3081817842","https://openalex.org/W3093282011","https://openalex.org/W3094795122","https://openalex.org/W3136354122","https://openalex.org/W3156316671","https://openalex.org/W4239287623","https://openalex.org/W4289744670","https://openalex.org/W6757639450"],"related_works":["https://openalex.org/W4312417841","https://openalex.org/W4321369474","https://openalex.org/W2731899572","https://openalex.org/W3133861977","https://openalex.org/W4200173597","https://openalex.org/W3116150086","https://openalex.org/W2401570230","https://openalex.org/W2999805992","https://openalex.org/W4310880831","https://openalex.org/W4386214304"],"abstract_inverted_index":{"Nowadays,":[0],"artificial":[1,56],"intelligence":[2,57],"(AI)":[3],"provides":[4],"tremendous":[5,69],"prospects":[6],"for":[7,110],"driving":[8],"future":[9],"healthcare":[10,22,29],"while":[11],"empowering":[12],"patients":[13],"and":[14,58,73,81,143,147,165,189],"service":[15],"providers.":[16],"The":[17,132,156,169],"extensive":[18],"use":[19],"of":[20,27],"digital":[21],"produces":[23],"a":[24,47,117,135,148],"massive":[25],"amount":[26],"multimedia":[28],"data":[30,42],"continuously":[31],"(e.g.,":[32],"MRI,":[33],"X-Ray,":[34],"ultrasound":[35],"images,":[36],"etc.).":[37],"Hence,":[38],"it":[39],"needs":[40],"special":[41],"analytics":[43],"techniques":[44],"to":[45,50,90,101,121,126],"provide":[46],"smart":[48],"diagnosis":[49,71],"the":[51,84,140,181],"patients.":[52],"Recent":[53],"advancements":[54],"in":[55,139,186],"machine":[59],"learning":[60,64,99,119],"techniques,":[61],"particularly":[62],"Deep":[63],"(DL)":[65],"methods,":[66],"have":[67,94],"demonstrated":[68],"medical":[70],"progress":[72],"achievements.":[74],"Diabetic":[75],"Retinopathy":[76],"(DR),":[77],"cataract,":[78],"macular":[79],"degeneration,":[80],"glaucoma":[82],"are":[83],"most":[85],"common":[86],"eye":[87],"problems":[88],"due":[89],"diabetes.":[91],"Numerous":[92],"models":[93,100],"been":[95],"proposed":[96,182],"using":[97],"deep":[98,118],"diagnose":[102],"diabetic":[103,123],"retinopathy,":[104],"but":[105],"no":[106],"model":[107,120,133,170,183],"is":[108,171],"perfect":[109],"detecting":[111],"DR":[112,128],"diseases.":[113],"This":[114],"article":[115],"presents":[116],"analyze":[122],"retinopathy":[124],"images":[125],"classify":[127],"patients\u2019":[129],"severity":[130],"levels.":[131],"applies":[134],"custom-weighted":[136],"loss":[137],"function":[138],"model\u2019s":[141,157],"training":[142],"achieves":[144],"92.49%":[145],"accuracy":[146,187],"0.945":[149],"Cohen":[150,190],"Kappa":[151,191],"score":[152,167],"on":[153],"test":[154],"data.":[155],"weighted":[158],"average":[159],"precision":[160],"was":[161],"93%,":[162],"recall":[163],"92%,":[164],"f1":[166],"93%.":[168],"compared":[172],"with":[173],"several":[174],"state-of-the-art":[175],"pre-trained":[176],"models.":[177],"We":[178],"observe":[179],"that":[180],"performs":[184],"better":[185],"results":[188],"score.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}