{"id":"https://openalex.org/W4317840304","doi":"https://doi.org/10.3233/jifs-224229","title":"Attention aware fully convolutional deep learning model for retinal blood vessel segmentation","display_name":"Attention aware fully convolutional deep learning model for retinal blood vessel segmentation","publication_year":2023,"publication_date":"2023-01-24","ids":{"openalex":"https://openalex.org/W4317840304","doi":"https://doi.org/10.3233/jifs-224229"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-224229","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-224229","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy Systems","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/A5012767334","display_name":"C Gobinath","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"C. Gobinath","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India"],"raw_orcid":"https://orcid.org/0000-0003-4562-9876","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073393602","display_name":"M. P. Gopinath","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"M.P. Gopinath","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India"],"raw_orcid":"https://orcid.org/0000-0002-1332-1060","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5073393602"],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":null,"apc_paid":null,"fwci":1.5623,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.8234832,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"44","issue":"4","first_page":"6413","last_page":"6423"},"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/T12599","display_name":"Retinal and Optic Conditions","score":0.9916999936103821,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9897000193595886,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8528587818145752},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.801999568939209},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7150224447250366},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7112260460853577},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6142929792404175},{"id":"https://openalex.org/keywords/fundus","display_name":"Fundus (uterus)","score":0.5809195637702942},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5305809378623962},{"id":"https://openalex.org/keywords/retinal","display_name":"Retinal","score":0.47130143642425537},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4504292607307434},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.44350603222846985},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43872061371803284},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4337741732597351},{"id":"https://openalex.org/keywords/diabetic-retinopathy","display_name":"Diabetic retinopathy","score":0.41549813747406006},{"id":"https://openalex.org/keywords/ophthalmology","display_name":"Ophthalmology","score":0.20445775985717773},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1456720530986786}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8528587818145752},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.801999568939209},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7150224447250366},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7112260460853577},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6142929792404175},{"id":"https://openalex.org/C2776391266","wikidata":"https://www.wikidata.org/wiki/Q9612","display_name":"Fundus (uterus)","level":2,"score":0.5809195637702942},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5305809378623962},{"id":"https://openalex.org/C2780827179","wikidata":"https://www.wikidata.org/wiki/Q422001","display_name":"Retinal","level":2,"score":0.47130143642425537},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4504292607307434},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.44350603222846985},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43872061371803284},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4337741732597351},{"id":"https://openalex.org/C2779829184","wikidata":"https://www.wikidata.org/wiki/Q631361","display_name":"Diabetic retinopathy","level":3,"score":0.41549813747406006},{"id":"https://openalex.org/C118487528","wikidata":"https://www.wikidata.org/wiki/Q161437","display_name":"Ophthalmology","level":1,"score":0.20445775985717773},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1456720530986786},{"id":"https://openalex.org/C134018914","wikidata":"https://www.wikidata.org/wiki/Q162606","display_name":"Endocrinology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jifs-224229","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-224229","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.5799999833106995,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W2072130234","https://openalex.org/W2801013643","https://openalex.org/W2801475611","https://openalex.org/W2809144587","https://openalex.org/W2911558038","https://openalex.org/W2941942490","https://openalex.org/W2955571529","https://openalex.org/W2966185236","https://openalex.org/W2998864442","https://openalex.org/W3004482288","https://openalex.org/W3006026428","https://openalex.org/W3043116777","https://openalex.org/W3048451585","https://openalex.org/W3091119025","https://openalex.org/W3093569756","https://openalex.org/W3126984895","https://openalex.org/W3131984632","https://openalex.org/W3135765136","https://openalex.org/W3144660641","https://openalex.org/W3155737436","https://openalex.org/W3159298823","https://openalex.org/W3172603679","https://openalex.org/W3178963004","https://openalex.org/W3205707234","https://openalex.org/W3211713681","https://openalex.org/W4200067518","https://openalex.org/W4200507480","https://openalex.org/W4205943221","https://openalex.org/W4206773083","https://openalex.org/W4213182863","https://openalex.org/W4214629996","https://openalex.org/W4229459202","https://openalex.org/W4283215393","https://openalex.org/W6617145748","https://openalex.org/W6791249563"],"related_works":["https://openalex.org/W1980571360","https://openalex.org/W186124986","https://openalex.org/W3176448898","https://openalex.org/W3167510834","https://openalex.org/W4379115808","https://openalex.org/W3207986206","https://openalex.org/W2429379476","https://openalex.org/W2744576618","https://openalex.org/W1979866946","https://openalex.org/W4226141369"],"abstract_inverted_index":{"Recent":[0],"reports":[1],"indicate":[2],"a":[3,35],"rise":[4],"in":[5,45,121],"retinal":[6,25,46,55],"issues,":[7],"and":[8,29,37,83,128,134,155],"automatic":[9],"artery":[10,70,96],"vein":[11],"categorization":[12],"offers":[13],"data":[14],"that":[15,140],"is":[16,93,144],"particularly":[17],"instructive":[18],"for":[19,42,89,113],"the":[20,54,79,141,153],"medical":[21],"evaluation":[22],"of":[23,123,146],"serious":[24],"disorders":[26],"including":[27],"glaucoma":[28],"diabetic":[30],"retinopathy.":[31],"This":[32,49],"work":[33],"presents":[34],"competent":[36],"precise":[38],"deep-learning":[39],"model":[40],"designed":[41],"vessel":[43,104,149],"segmentation":[44],"fundus":[47],"imaging.":[48],"article":[50],"aims":[51],"to":[52,77],"segment":[53],"images":[56],"using":[57],"an":[58],"attention-based":[59],"dense":[60,84],"fully":[61],"convolutional":[62,75],"neural":[63],"network":[64],"(A-DFCNN)":[65],"after":[66],"removing":[67],"uncertainty.":[68],"The":[69,115,137],"extraction":[71,97],"layers":[72,85,98],"encompass":[73],"vessel-specific":[74],"blocks":[76],"focus":[78],"tiny":[80],"blood":[81],"vessels":[82],"with":[86,95],"skip":[87],"connections":[88],"feature":[90],"propagation.":[91],"Segmentation":[92],"associated":[94],"via":[99],"individual":[100,108],"loss":[101,109],"function.":[102],"Blood":[103],"maps":[105],"produced":[106],"from":[107,131],"functions":[110],"are":[111],"authenticated":[112],"performance.":[114],"proposed":[116,142],"technique":[117],"attains":[118],"improved":[119],"outcomes":[120],"terms":[122],"Accuracy":[124],"(0.9834),":[125],"Sensitivity":[126],"(0.8553),":[127],"Specificity":[129],"(0.9835)":[130],"DRIVE,":[132],"STARE,":[133],"CHASE-DB1":[135],"datasets.":[136],"result":[138],"demonstrates":[139],"A-DFCNN":[143],"capable":[145],"segmenting":[147],"minute":[148],"bifurcation":[150],"breakdowns":[151],"during":[152],"training":[154],"testing":[156],"phases.":[157]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}