{"id":"https://openalex.org/W7223955563","doi":"https://doi.org/10.4015/s1016237226500286","title":"TRANSFER LEARNING-BASED DEEP ARCHITECTURE FOR VITILIGO DETECTION WITH AN IMPROVED SEGNET MODEL","display_name":"TRANSFER LEARNING-BASED DEEP ARCHITECTURE FOR VITILIGO DETECTION WITH AN IMPROVED SEGNET MODEL","publication_year":2026,"publication_date":"2026-10-27","ids":{"openalex":"https://openalex.org/W7223955563","doi":"https://doi.org/10.4015/s1016237226500286"},"language":"en","primary_location":{"id":"doi:10.4015/s1016237226500286","doi":"https://doi.org/10.4015/s1016237226500286","is_oa":false,"landing_page_url":"https://doi.org/10.4015/s1016237226500286","pdf_url":null,"source":{"id":"https://openalex.org/S1040749385","display_name":"Biomedical Engineering Applications Basis and Communications","issn_l":"1016-2372","issn":["1016-2372","1793-7132"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","poland-20","russia-white-list-4"],"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":"Biomedical Engineering: Applications, Basis and Communications","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"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/A5109050174","display_name":"Priyanka Prabhakar Pawar","orcid":"https://orcid.org/0000-0002-2647-7494","observed_orcids":["https://orcid.org/0000-0002-2647-7494"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Priyanka Prabhakar Pawar","raw_affiliation_strings":["School of Computer Science and Engineering, Sandip University, Nashik, Maharashtra 422213, India"],"raw_orcid":"https://orcid.org/0000-0002-2647-7494","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Sandip University, Nashik, Maharashtra 422213, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068662462","display_name":"Rais Abdul Hamid Khan","orcid":"https://orcid.org/0000-0003-2604-6851","observed_orcids":["https://orcid.org/0000-0003-2604-6851"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rais Abdul Hamid Khan","raw_affiliation_strings":["School of Computer Science and Engineering, Sandip University, Nashik, Maharashtra 422213, India"],"raw_orcid":"https://orcid.org/0000-0003-2604-6851","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Sandip University, Nashik, Maharashtra 422213, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.81144052,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.22360000014305115,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.22360000014305115,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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":0.20020000636577606,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.19040000438690186,"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/transfer-learning","display_name":"transfer learning","score":0.7950000166893005},{"id":"https://openalex.org/keywords/segnet","display_name":"SegNet","score":0.8479999899864197},{"id":"https://openalex.org/keywords/skin-lesion-segmentation","display_name":"skin lesion segmentation","score":0.6940000057220459},{"id":"https://openalex.org/keywords/googlenet","display_name":"GoogLeNet","score":0.7409999966621399},{"id":"https://openalex.org/keywords/anisotropic-diffusion-filter","display_name":"anisotropic diffusion filter","score":0.8019999861717224},{"id":"https://openalex.org/keywords/wiener-filter","display_name":"Wiener filter","score":0.6959999799728394},{"id":"https://openalex.org/keywords/multiscale-feature-extraction","display_name":"multi-scale feature extraction","score":0.8029999732971191},{"id":"https://openalex.org/keywords/depthwise-separable-convolution","display_name":"depthwise separable convolution","score":0.9330000281333923},{"id":"https://openalex.org/keywords/texture-features","display_name":"texture features","score":0.6850000023841858},{"id":"https://openalex.org/keywords/gray-level-co-occurrence-matrix","display_name":"gray level co-occurrence matrix","score":0.8460000157356262},{"id":"https://openalex.org/keywords/rgb-histogram","display_name":"RGB histogram","score":0.7419999837875366}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8299000263214111},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.66839998960495},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6531000137329102},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5856000185012817},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5019999742507935},{"id":"https://openalex.org/C84216515","wikidata":"https://www.wikidata.org/wiki/Q375677","display_name":"RGB color space","level":5,"score":0.47870001196861267},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4758000075817108},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4641000032424927},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4514000117778778},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44209998846054077},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.41510000824928284},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3702999949455261},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.35199999809265137},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3467999994754791},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.34220001101493835},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.33869999647140503},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.32010000944137573},{"id":"https://openalex.org/C203504353","wikidata":"https://www.wikidata.org/wiki/Q4765461","display_name":"Anisotropic diffusion","level":3,"score":0.31040000915527344},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C2961294","wikidata":"https://www.wikidata.org/wiki/Q166863","display_name":"Color space","level":3,"score":0.302700012922287},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.26910001039505005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.4015/s1016237226500286","doi":"https://doi.org/10.4015/s1016237226500286","is_oa":false,"landing_page_url":"https://doi.org/10.4015/s1016237226500286","pdf_url":null,"source":{"id":"https://openalex.org/S1040749385","display_name":"Biomedical Engineering Applications Basis and Communications","issn_l":"1016-2372","issn":["1016-2372","1793-7132"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","poland-20","russia-white-list-4"],"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":"Biomedical Engineering: Applications, Basis and Communications","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":0.9391000270843506}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4415034251","https://openalex.org/W7163045896","https://openalex.org/W4416077201","https://openalex.org/W7162204330","https://openalex.org/W7135243797","https://openalex.org/W4394989200","https://openalex.org/W7124756638","https://openalex.org/W4408304091","https://openalex.org/W4414561285","https://openalex.org/W7125530315"],"abstract_inverted_index":{"As":[0],"a":[1,21,129,205,225,277],"chronic":[2],"condition":[3],"resulting":[4],"in":[5,38,88,108,132],"pigmentation":[6],"loss,":[7],"vitiligo":[8,36,84,274],"requires":[9],"early":[10],"and":[11,31,65,72,101,147,195,237,253,280,302,325],"precise":[12,163],"detection":[13,85,212],"for":[14,35,83,99,105,208,233,249,284],"effective":[15],"medical":[16,285],"intervention.":[17],"This":[18,268],"work":[19],"presents":[20],"dual-stage":[22],"deep":[23,80],"learning":[24,81,260],"system":[25],"that":[26,43,56],"combines":[27],"improved":[28],"preprocessing,":[29],"segmentation,":[30],"classification":[32,66,254],"specifically":[33],"designed":[34],"detection,":[37,275],"contrast":[39],"to":[40,137,162,272],"previous":[41],"research":[42,269],"depends":[44],"on":[45],"traditional":[46],"CNN":[47],"architectures":[48],"or":[49],"single-stage":[50],"pipelines.":[51],"An":[52],"innovative":[53],"architectural":[54],"development":[55],"greatly":[57],"raises":[58],"the":[59,68,75,109,121,170,211,294,309,313],"accuracy":[60,264],"of":[61,70,124,165,320,323,327],"multi-scale":[62,158,234],"feature":[63,171,176,235],"extraction":[64,172,236],"is":[67,86,118,222],"incorporation":[69],"FSMSDSCM":[71],"WESELU":[73],"into":[74],"IGNT-TL":[76,295,310],"model.":[77],"A":[78],"new":[79],"approach":[82,261],"proposed":[87,257],"this":[89],"paper,":[90],"leveraging":[91],"Transfer":[92,219],"Learning":[93,220],"with":[94,128,218,317],"an":[95,102,112,153,214,238,318],"Improved":[96,113,215],"SegNet":[97],"(ImSNT)":[98],"segmentation":[100,150,164],"optimized":[103,239],"GoogleNet":[104,216],"classification.":[106,209],"Initially,":[107],"preprocessing":[110],"stage,":[111,173,213],"Anisotropic":[114,133],"Filtering":[115,135],"(IAF)":[116],"technique":[117],"introduced,":[119,223],"incorporating":[120],"Inverse":[122],"Laplace":[123],"Gaussian":[125],"(ILOG)":[126],"transformation":[127],"Wiener":[130],"filter":[131],"Diffusion":[134],"(ADF)":[136],"effectively":[138],"remove":[139],"noise":[140],"while":[141,265],"preserving":[142],"essential":[143],"details":[144],"like":[145],"edges":[146],"textures.":[148],"The":[149,256,288],"stage":[151],"employs":[152],"ImSNT":[154],"model,":[155],"which":[156],"captures":[157],"spatial":[159],"information,":[160],"leading":[161],"vitiligo-affected":[166],"skin":[167],"regions.":[168],"In":[169,210,307],"four":[174],"crucial":[175],"types":[177],"are":[178],"derived":[179],"from":[180],"segmented":[181],"images:":[182],"texture":[183],"features":[184,197,203],"using":[185,198],"Gray-Level":[186],"Co-occurrence":[187],"Matrix":[188],"(GLCM),":[189],"disease":[190],"area":[191],"features,":[192,194],"pixel-based":[193],"color":[196],"RGB":[199],"histograms.":[200],"These":[201],"extracted":[202],"form":[204],"comprehensive":[206],"dataset":[207],"model":[217,263,311],"(IGNT-TL)":[221],"utilizing":[224],"Feature":[226],"Slicing-Multisize":[227],"Depthwise":[228],"Separable":[229],"Convolution":[230],"Module":[231],"(FSMSDSCM)":[232],"Weighted":[240],"ELiSH":[241],"Scaled":[242],"Exponential":[243],"Linear":[244],"Unit":[245],"(WESELU)":[246],"activation":[247],"function":[248],"better":[250],"gradient":[251],"propagation":[252],"performance.":[255],"two-stage":[258],"transfer":[259],"enhances":[262],"reducing":[266],"overfitting.":[267],"significantly":[270],"contributes":[271],"automated":[273],"providing":[276],"robust,":[278],"efficient,":[279],"accurate":[281],"DL":[282],"framework":[283],"image":[286],"analysis.":[287],"highest":[289],"performance":[290],"was":[291],"recorded":[292],"by":[293],"strategy,":[296],"achieving":[297],"0.964":[298],"accuracy,":[299],"0.980":[300],"precision,":[301],"0.968":[303],"[Formula:":[304],"see":[305],"text]-measure.":[306],"addition,":[308],"exhibited":[312],"least":[314],"error":[315],"rates,":[316],"FNR":[319],"0.044,":[321],"FPR":[322],"0.025,":[324],"FDR":[326],"0.020.":[328]},"counts_by_year":[],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2026-10-28T00:00:00"}