{"id":"https://openalex.org/W3111185497","doi":"https://doi.org/10.1515/bams-2020-0053","title":"Automated diagnosis of diabetic retinopathy enabled by optimized thresholding-based blood vessel segmentation and hybrid classifier","display_name":"Automated diagnosis of diabetic retinopathy enabled by optimized thresholding-based blood vessel segmentation and hybrid classifier","publication_year":2020,"publication_date":"2020-12-07","ids":{"openalex":"https://openalex.org/W3111185497","doi":"https://doi.org/10.1515/bams-2020-0053","mag":"3111185497"},"language":"en","primary_location":{"id":"doi:10.1515/bams-2020-0053","is_oa":false,"landing_page_url":"https://doi.org/10.1515/bams-2020-0053","pdf_url":null,"source":{"id":"https://openalex.org/S2764861201","display_name":"Bio-Algorithms and Med-Systems","issn_l":"1895-9091","issn":["1895-9091","1896-530X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310313990","host_organization_name":"De Gruyter","host_organization_lineage":["https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bio-Algorithms and Med-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/A5073578512","display_name":"Bansode Balbhim Narhari","orcid":null},"institutions":[{"id":"https://openalex.org/I165259314","display_name":"Dr. Babasaheb Ambedkar Marathwada University","ror":"https://ror.org/033pfj584","country_code":"IN","type":"education","lineage":["https://openalex.org/I165259314"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Bansode Balbhim Narhari","raw_affiliation_strings":["Department of Electronics & Telecommunication Engineering , MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University , Aurangabad , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics & Telecommunication Engineering , MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University , Aurangabad , India","institution_ids":["https://openalex.org/I165259314"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027557137","display_name":"Bakwad Kamlakar Murlidhar","orcid":null},"institutions":[{"id":"https://openalex.org/I2799436172","display_name":"Maharashtra State Board of Technical Education","ror":"https://ror.org/03af8h728","country_code":"IN","type":"education","lineage":["https://openalex.org/I2799436172"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bakwad Kamlakar Murlidhar","raw_affiliation_strings":["Department of Electronics Engineering , Puranmal Lahoti Govt. Polytechnic College, MSBTE, Latur , Mumbai , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics Engineering , Puranmal Lahoti Govt. Polytechnic College, MSBTE, Latur , Mumbai , India","institution_ids":["https://openalex.org/I2799436172"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072285558","display_name":"Sayyad Ajij","orcid":"https://orcid.org/0000-0003-1874-7885"},"institutions":[{"id":"https://openalex.org/I165259314","display_name":"Dr. Babasaheb Ambedkar Marathwada University","ror":"https://ror.org/033pfj584","country_code":"IN","type":"education","lineage":["https://openalex.org/I165259314"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ajij Dildar Sayyad","raw_affiliation_strings":["Department of Electronics & Telecommunication Engineering , MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University , Aurangabad , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics & Telecommunication Engineering , MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University , Aurangabad , India","institution_ids":["https://openalex.org/I165259314"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038213006","display_name":"Ganesh Shahubha Sable","orcid":null},"institutions":[{"id":"https://openalex.org/I165259314","display_name":"Dr. Babasaheb Ambedkar Marathwada University","ror":"https://ror.org/033pfj584","country_code":"IN","type":"education","lineage":["https://openalex.org/I165259314"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ganesh Shahubha Sable","raw_affiliation_strings":["Department of Electronics & Telecommunication Engineering , MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University , Aurangabad , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics & Telecommunication Engineering , MIT College of Engineering, Dr. Babasaheb Ambedkar Marathwada University , Aurangabad , India","institution_ids":["https://openalex.org/I165259314"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5073578512"],"corresponding_institution_ids":["https://openalex.org/I165259314"],"apc_list":null,"apc_paid":null,"fwci":0.3523,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.65794956,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"17","issue":"1","first_page":"9","last_page":"23"},"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/T10170","display_name":"Retinal Diseases and Treatments","score":0.989799976348877,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9778000116348267,"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":0.8324328064918518},{"id":"https://openalex.org/keywords/adaptive-histogram-equalization","display_name":"Adaptive histogram equalization","score":0.759082555770874},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.7455015182495117},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6856095790863037},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6796591281890869},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6559526920318604},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6404215097427368},{"id":"https://openalex.org/keywords/diabetic-retinopathy","display_name":"Diabetic retinopathy","score":0.5151087641716003},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4904586970806122},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46633386611938477},{"id":"https://openalex.org/keywords/fundus","display_name":"Fundus (uterus)","score":0.45915642380714417},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.44674527645111084},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.3899770677089691},{"id":"https://openalex.org/keywords/histogram-equalization","display_name":"Histogram equalization","score":0.21584832668304443},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1607169210910797},{"id":"https://openalex.org/keywords/ophthalmology","display_name":"Ophthalmology","score":0.11087876558303833},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.10984617471694946},{"id":"https://openalex.org/keywords/diabetes-mellitus","display_name":"Diabetes mellitus","score":0.10941699147224426}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8324328064918518},{"id":"https://openalex.org/C30387639","wikidata":"https://www.wikidata.org/wiki/Q4680744","display_name":"Adaptive histogram equalization","level":5,"score":0.759082555770874},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.7455015182495117},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6856095790863037},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6796591281890869},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6559526920318604},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6404215097427368},{"id":"https://openalex.org/C2779829184","wikidata":"https://www.wikidata.org/wiki/Q631361","display_name":"Diabetic retinopathy","level":3,"score":0.5151087641716003},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4904586970806122},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46633386611938477},{"id":"https://openalex.org/C2776391266","wikidata":"https://www.wikidata.org/wiki/Q9612","display_name":"Fundus (uterus)","level":2,"score":0.45915642380714417},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44674527645111084},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.3899770677089691},{"id":"https://openalex.org/C136943445","wikidata":"https://www.wikidata.org/wiki/Q1970240","display_name":"Histogram equalization","level":4,"score":0.21584832668304443},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1607169210910797},{"id":"https://openalex.org/C118487528","wikidata":"https://www.wikidata.org/wiki/Q161437","display_name":"Ophthalmology","level":1,"score":0.11087876558303833},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.10984617471694946},{"id":"https://openalex.org/C555293320","wikidata":"https://www.wikidata.org/wiki/Q12206","display_name":"Diabetes mellitus","level":2,"score":0.10941699147224426},{"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:10.1515/bams-2020-0053","is_oa":false,"landing_page_url":"https://doi.org/10.1515/bams-2020-0053","pdf_url":null,"source":{"id":"https://openalex.org/S2764861201","display_name":"Bio-Algorithms and Med-Systems","issn_l":"1895-9091","issn":["1895-9091","1896-530X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310313990","host_organization_name":"De Gruyter","host_organization_lineage":["https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bio-Algorithms and Med-Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.46000000834465027,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1846171835","https://openalex.org/W1973351628","https://openalex.org/W2061438946","https://openalex.org/W2077913352","https://openalex.org/W2103496373","https://openalex.org/W2110119381","https://openalex.org/W2139923324","https://openalex.org/W2150091941","https://openalex.org/W2215003561","https://openalex.org/W2226754619","https://openalex.org/W2290883490","https://openalex.org/W2306115793","https://openalex.org/W2313516491","https://openalex.org/W2507548850","https://openalex.org/W2558098092","https://openalex.org/W2574864336","https://openalex.org/W2598442119","https://openalex.org/W2619391717","https://openalex.org/W2761604622","https://openalex.org/W2770657900","https://openalex.org/W2783066242","https://openalex.org/W2790576426","https://openalex.org/W2791485417","https://openalex.org/W2792119716","https://openalex.org/W2793597870","https://openalex.org/W2887928931","https://openalex.org/W2888424632","https://openalex.org/W2901075482","https://openalex.org/W2910567234","https://openalex.org/W2919733445","https://openalex.org/W2921015097","https://openalex.org/W2952436003","https://openalex.org/W2953618482","https://openalex.org/W2972689037","https://openalex.org/W2980444207","https://openalex.org/W2991315466","https://openalex.org/W2995366209","https://openalex.org/W3011401450","https://openalex.org/W3014804140"],"related_works":["https://openalex.org/W2054831422","https://openalex.org/W2106731176","https://openalex.org/W2387104004","https://openalex.org/W2057981026","https://openalex.org/W2165297163","https://openalex.org/W3047671631","https://openalex.org/W1980571360","https://openalex.org/W1548186045","https://openalex.org/W2902098370","https://openalex.org/W1838530225"],"abstract_inverted_index":{"Abstract":[0],"Objectives":[1],"The":[2,33,75,226,274,297],"focus":[3],"of":[4,35,55,62,89,92,103,105,113,204,211],"this":[5,133],"paper":[6],"is":[7,37,165,180,206,215,238],"to":[8,47,143,182,231,240],"introduce":[9],"an":[10],"automated":[11],"early":[12,90,108],"Diabetic":[13],"Retinopathy":[14],"(DR)":[15],"detection":[16,109,271,277,304],"scheme":[17],"from":[18,39,73],"colour":[19],"fundus":[20,138],"images":[21,128,139,205],"through":[22,208],"enhanced":[23,249],"segmentation":[24,164,244],"and":[25,94,101,110,156,194,219,233,245,284,294],"classification":[26,203,246],"strategies":[27],"by":[28,80,149,250],"analyzing":[29],"blood":[30,168],"vessels.":[31],"Methods":[32],"occurrence":[34,102],"DR":[36,81,93,114,130,276],"increasing":[38],"the":[40,44,52,59,63,87,99,107,135,160,172,186,202,209,220,234,243,251,285,302],"past":[41],"years,":[42],"impacting":[43],"eyes":[45],"due":[46],"a":[48],"sudden":[49],"rise":[50],"in":[51,262,281,306],"glucose":[53],"level":[54],"blood.":[56],"All":[57],"over":[58],"world,":[60],"half":[61],"people":[64],"who":[65,77],"are":[66,70,78,115,140,199,229,248,266],"under":[67],"age":[68],"70":[69],"severely":[71],"suffered":[72],"diabetes.":[74],"patients":[76],"affected":[79],"will":[82],"lose":[83],"their":[84],"vision":[85],"during":[86],"absence":[88],"recognition":[91],"appropriate":[95],"treatment.":[96],"To":[97],"decrease":[98],"growth":[100],"loss":[104],"vision,":[106],"timely":[111],"treatment":[112],"desirable.":[116],"At":[117],"present,":[118],"deep":[119],"learning":[120],"models":[121],"have":[122],"presented":[123],"better":[124],"performance":[125],"using":[126,289],"retinal":[127,137],"for":[129,167,268],"detection.":[131],"In":[132,185],"work,":[134],"input":[136],"initially":[141],"subjected":[142,230,239],"pre-processing":[144],"that":[145],"undergoes":[146],"contrast":[147],"enhancement":[148],"Contrast":[150],"Limited":[151],"Adaptive":[152],"Histogram":[153],"Equalization":[154],"(CLAHE)":[155],"average":[157],"filtering.":[158],"Further,":[159],"optimized":[161,267],"binary":[162],"thresholding-based":[163],"done":[166,207,288],"vessel":[169],"segmentation.":[170],"For":[171],"segmented":[173,236],"image,":[174],"Tri-level":[175],"Discrete":[176],"Level":[177],"Decomposition":[178],"(Tri-DWT)":[179],"performed":[181],"decompose":[183],"it.":[184],"feature":[187],"extraction":[188],"phase,":[189],"Local":[190],"Binary":[191],"Pattern":[192],"(LBP),":[193],"Gray-Level":[195],"Co-occurrence":[196],"Matrices":[197],"(GLCMs)":[198],"extracted.":[200],"Next,":[201],"combination":[210],"two":[212],"algorithms,":[213],"one":[214],"Neural":[216,223],"Network":[217,224],"(NN),":[218],"other":[221],"Convolutional":[222],"(CNN).":[225],"extracted":[227],"features":[228],"NN,":[232],"tri-DWT-based":[235],"image":[237],"CNN.":[241],"Both":[242],"phases":[247],"improved":[252],"meta-heuristic":[253],"algorithm":[254,300],"called":[255],"Fitness":[256],"Rate-based":[257],"Crow":[258],"Search":[259],"Algorithm":[260],"(FR-CSA),":[261],"which":[263],"few":[264],"parameters":[265],"attaining":[269],"maximum":[270],"accuracy.":[272],"Results":[273],"proposed":[275],"model":[278],"was":[279,287],"implemented":[280],"MATLAB":[282],"2018a,":[283],"analysis":[286],"three":[290],"datasets,":[291],"HRF,":[292],"Messidor,":[293],"DIARETDB.":[295],"Conclusions":[296],"developed":[298],"FR-CSA":[299],"has":[301],"best":[303],"accuracy":[305],"diagnosing":[307],"DR.":[308]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}
