{"id":"https://openalex.org/W4281772541","doi":"https://doi.org/10.1142/s0218001422520176","title":"Deep Learning-Based Verification of Iridology in Diagnosing Type II Diabetes Mellitus","display_name":"Deep Learning-Based Verification of Iridology in Diagnosing Type II Diabetes Mellitus","publication_year":2022,"publication_date":"2022-06-04","ids":{"openalex":"https://openalex.org/W4281772541","doi":"https://doi.org/10.1142/s0218001422520176"},"language":"en","primary_location":{"id":"doi:10.1142/s0218001422520176","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001422520176","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","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/A5101543043","display_name":"K Sruthi","orcid":null},"institutions":[{"id":"https://openalex.org/I111575329","display_name":"Bharathiar University","ror":"https://ror.org/04fht8c22","country_code":"IN","type":"education","lineage":["https://openalex.org/I111575329"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"K. Sruthi","raw_affiliation_strings":["Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, Tamil Nadu, India"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, Tamil Nadu, India","institution_ids":["https://openalex.org/I111575329"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111280070","display_name":"J Vijayakumar","orcid":null},"institutions":[{"id":"https://openalex.org/I111575329","display_name":"Bharathiar University","ror":"https://ror.org/04fht8c22","country_code":"IN","type":"education","lineage":["https://openalex.org/I111575329"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"J. Vijayakumar","raw_affiliation_strings":["Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, Tamil Nadu, India"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, Tamil Nadu, India","institution_ids":["https://openalex.org/I111575329"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109876088","display_name":"S. Thavamani","orcid":null},"institutions":[{"id":"https://openalex.org/I111575329","display_name":"Bharathiar University","ror":"https://ror.org/04fht8c22","country_code":"IN","type":"education","lineage":["https://openalex.org/I111575329"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"S. Thavamani","raw_affiliation_strings":["Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, Tamil Nadu, India"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, Tamil Nadu, India","institution_ids":["https://openalex.org/I111575329"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101543043"],"corresponding_institution_ids":["https://openalex.org/I111575329"],"apc_list":null,"apc_paid":null,"fwci":0.7393,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.73376415,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"36","issue":"11","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12647","display_name":"Traditional Chinese Medicine Studies","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative medicine"},"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/T12647","display_name":"Traditional Chinese Medicine Studies","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative medicine"},"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/T11327","display_name":"Acupuncture Treatment Research Studies","score":0.963100016117096,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative medicine"},"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/T10828","display_name":"Biometric Identification and Security","score":0.9478999972343445,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.7569565176963806},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6470212936401367},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6447130441665649},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5490560531616211},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5426303744316101},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5290511846542358},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5240721106529236},{"id":"https://openalex.org/keywords/iris","display_name":"IRIS (biosensor)","score":0.45811620354652405},{"id":"https://openalex.org/keywords/type-2-diabetes-mellitus","display_name":"Type 2 Diabetes Mellitus","score":0.4133864939212799},{"id":"https://openalex.org/keywords/diabetes-mellitus","display_name":"Diabetes mellitus","score":0.35570234060287476},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3488565683364868},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.08378812670707703}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7569565176963806},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6470212936401367},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6447130441665649},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5490560531616211},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5426303744316101},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5290511846542358},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5240721106529236},{"id":"https://openalex.org/C2779503344","wikidata":"https://www.wikidata.org/wiki/Q5973514","display_name":"IRIS (biosensor)","level":3,"score":0.45811620354652405},{"id":"https://openalex.org/C2910068830","wikidata":"https://www.wikidata.org/wiki/Q3025883","display_name":"Type 2 Diabetes Mellitus","level":3,"score":0.4133864939212799},{"id":"https://openalex.org/C555293320","wikidata":"https://www.wikidata.org/wiki/Q12206","display_name":"Diabetes mellitus","level":2,"score":0.35570234060287476},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3488565683364868},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.08378812670707703},{"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:10.1142/s0218001422520176","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001422520176","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1899795980","https://openalex.org/W2008196698","https://openalex.org/W2025422703","https://openalex.org/W2066142276","https://openalex.org/W2094830902","https://openalex.org/W2118282683","https://openalex.org/W2119972013","https://openalex.org/W2139967137","https://openalex.org/W2146880726","https://openalex.org/W2151341741","https://openalex.org/W2317637439","https://openalex.org/W2405218076","https://openalex.org/W2609422230","https://openalex.org/W2741839910","https://openalex.org/W2765685479","https://openalex.org/W2778726223","https://openalex.org/W2783741365","https://openalex.org/W2802806477","https://openalex.org/W2907088611","https://openalex.org/W2946398170","https://openalex.org/W2955740297","https://openalex.org/W2981565591","https://openalex.org/W2989278094","https://openalex.org/W2995053605","https://openalex.org/W3011990345","https://openalex.org/W3041123799","https://openalex.org/W3107636056","https://openalex.org/W3216633527","https://openalex.org/W4230888393","https://openalex.org/W4254045779"],"related_works":["https://openalex.org/W2764306564","https://openalex.org/W2465851997","https://openalex.org/W4293226380","https://openalex.org/W4300631340","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Type":[0,48,96,122,178,253],"II":[1,5,49,97,123,179,254],"Diabetes":[2],"Mellitus":[3],"(Type":[4],"DM)":[6],"is":[7,73],"a":[8,27,41,203,226],"chronic":[9],"condition":[10],"that":[11],"has":[12,35],"detrimental":[13],"effect":[14],"on":[15,162],"vital":[16],"organs":[17],"if":[18],"left":[19],"untreated,":[20],"necessitating":[21],"early":[22,45],"diagnosis":[23,46],"and":[24,31,61,67,127,172,193,198,202,229,234,243],"treatment.":[25],"Iridology,":[26],"subset":[28],"of":[29,47,56,65,75,92,106,158,195,200,205,216,231,247],"Complementary":[30],"Alternative":[32],"Medicine":[33],"(CAM),":[34],"the":[36,54,76,90,163,241,245,248],"potential":[37],"to":[38,118,153,176],"serve":[39],"as":[40,59,169],"tool":[42],"for":[43,63,144,187,218,251],"noninvasive":[44],"DM.":[50,255],"Iridology":[51],"involves":[52],"analyzing":[53],"characteristics":[55],"iris":[57,107,111,146,149,188],"such":[58,168],"color":[60],"pattern":[62],"detection":[64],"organ":[66],"system":[68],"defects.":[69],"Deep":[70],"learning":[71,101],"algorithm":[72,138,250],"one":[74],"promising":[77],"methods":[78],"in":[79,94],"diagnosing":[80,95,252],"various":[81],"health-related":[82],"issues.":[83],"In":[84],"this":[85],"study,":[86],"we":[87],"have":[88,135],"demonstrated":[89],"efficiency":[91],"iridology":[93,164],"DM":[98,124,180],"using":[99,110,139,209],"deep":[100],"algorithms.":[102],"Near":[103],"Infra-Red":[104],"images":[105,150],"were":[108,151,174],"captured":[109],"scanner":[112],"from":[113],"178":[114],"voluntary":[115],"subjects":[116],"belonging":[117],"two":[119],"categories":[120],"namely,":[121],"(95":[125],"subjects)":[126],"nondiabetic":[128],"or":[129],"healthy":[130,182],"category":[131],"(83":[132],"subjects).":[133],"We":[134],"developed":[136],"an":[137,191],"Fully":[140],"Convolutional":[141],"Neural":[142],"network":[143],"effective":[145],"segmentation.":[147],"Normalized":[148],"used":[152,175],"crop":[154],"out":[155],"our":[156],"region":[157],"interest,":[159],"pancreas,":[160],"based":[161,220],"chart.":[165],"Classification":[166],"networks":[167],"AlexNet,":[170],"VGG-16,":[171],"ResNet-50":[173],"classify":[177],"versus":[181],"category.":[183],"Our":[184,237],"proposed":[185,249],"model":[186],"segmentation":[189],"achieved":[190],"accuracy":[192,215],"sensitivity":[194],"0.99,":[196],"specificity":[197],"F-Score":[199],"0.98,":[201],"precision":[204,230],"0.97.":[206],"Results":[207],"obtained":[208],"AlexNet":[210],"classifier":[211,224],"exhibits":[212],"better":[213],"classification":[214],"95.85%":[217],"Zero-padding":[219],"resized":[221],"image.":[222],"The":[223],"yielded":[225],"sensitivity,":[227],"specificity,":[228],"95.80%,":[232],"95.85%,":[233],"96.11%,":[235],"respectively.":[236],"study":[238],"results":[239],"establish":[240],"efficacy":[242],"emphasize":[244],"importance":[246]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
