{"id":"https://openalex.org/W4408611530","doi":"https://doi.org/10.1109/icaiic64266.2025.10920810","title":"STMFNet: Spatial Texture Multi-Scale Feature Fusion Attention Network for Diabetic Retinopathy Classification","display_name":"STMFNet: Spatial Texture Multi-Scale Feature Fusion Attention Network for Diabetic Retinopathy Classification","publication_year":2025,"publication_date":"2025-02-18","ids":{"openalex":"https://openalex.org/W4408611530","doi":"https://doi.org/10.1109/icaiic64266.2025.10920810"},"language":"en","primary_location":{"id":"doi:10.1109/icaiic64266.2025.10920810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic64266.2025.10920810","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5094198699","display_name":"MD Ilias Bappi","orcid":null},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"MD Ilias Bappi","raw_affiliation_strings":["Chonnam National University,Department of Artificial Intelligence Convergence,Gwangju,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chonnam National University,Department of Artificial Intelligence Convergence,Gwangju,South Korea","institution_ids":["https://openalex.org/I111277659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5098690812","display_name":"Md Monir Ahammod Bin Atique","orcid":"https://orcid.org/0009-0000-7103-9744"},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Md Monir Ahammod Bin Atique","raw_affiliation_strings":["Chonnam National University,Department of Artificial Intelligence Convergence,Gwangju,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chonnam National University,Department of Artificial Intelligence Convergence,Gwangju,South Korea","institution_ids":["https://openalex.org/I111277659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015500546","display_name":"Kyungbaek Kim","orcid":"https://orcid.org/0000-0001-9985-3051"},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kyungbaek Kim","raw_affiliation_strings":["Chonnam National University,Department of Artificial Intelligence Convergence,Gwangju,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chonnam National University,Department of Artificial Intelligence Convergence,Gwangju,South Korea","institution_ids":["https://openalex.org/I111277659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I111277659"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05130377,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"0504","last_page":"0509"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9998000264167786,"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":0.9998000264167786,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9616000056266785,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9570000171661377,"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":0.7074782848358154},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7012802958488464},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6777331829071045},{"id":"https://openalex.org/keywords/diabetic-retinopathy","display_name":"Diabetic retinopathy","score":0.5976324081420898},{"id":"https://openalex.org/keywords/texture","display_name":"Texture (cosmology)","score":0.5758393406867981},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.522392988204956},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.46402886509895325},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.44939735531806946},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4446724057197571},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3538683354854584},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.20164015889167786},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.131276935338974},{"id":"https://openalex.org/keywords/diabetes-mellitus","display_name":"Diabetes mellitus","score":0.10041847825050354},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.08679872751235962},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06738734245300293}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7074782848358154},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7012802958488464},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6777331829071045},{"id":"https://openalex.org/C2779829184","wikidata":"https://www.wikidata.org/wiki/Q631361","display_name":"Diabetic retinopathy","level":3,"score":0.5976324081420898},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.5758393406867981},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.522392988204956},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.46402886509895325},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.44939735531806946},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4446724057197571},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3538683354854584},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.20164015889167786},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.131276935338974},{"id":"https://openalex.org/C555293320","wikidata":"https://www.wikidata.org/wiki/Q12206","display_name":"Diabetes mellitus","level":2,"score":0.10041847825050354},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.08679872751235962},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06738734245300293},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaiic64266.2025.10920810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic64266.2025.10920810","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2169961704","https://openalex.org/W2194775991","https://openalex.org/W2531409750","https://openalex.org/W2769713325","https://openalex.org/W2906933145","https://openalex.org/W2924509510","https://openalex.org/W2964118901","https://openalex.org/W2982083293","https://openalex.org/W3133478370","https://openalex.org/W3168224088","https://openalex.org/W3176747664","https://openalex.org/W4285160408","https://openalex.org/W4382458216","https://openalex.org/W4386076493","https://openalex.org/W6739901393","https://openalex.org/W6762718338"],"related_works":["https://openalex.org/W3203337527","https://openalex.org/W2044207570","https://openalex.org/W1980571360","https://openalex.org/W4244634705","https://openalex.org/W3023629149","https://openalex.org/W2912406428","https://openalex.org/W2100530570","https://openalex.org/W4256153729","https://openalex.org/W4395042183","https://openalex.org/W1988509163"],"abstract_inverted_index":{"Diabetic":[0],"Retinopathy":[1],"(DR)":[2],"is":[3,178],"a":[4,78,128,181],"major":[5],"cause":[6],"of":[7,20,86,131,142],"vision":[8],"loss":[9],"and":[10,17,25,44,118,136,154,170,186],"blindness,":[11],"particularly":[12],"among":[13],"diabetic":[14],"patients.":[15],"Effective":[16],"timely":[18],"treatment":[19],"DR":[21,87,109,176,211],"relies":[22],"on":[23,102,199],"precise":[24],"automated":[26],"detection":[27],"systems":[28],"that":[29,192],"can":[30],"assess":[31],"disease":[32],"severity":[33],"from":[34,139],"retinal":[35],"fundus":[36,204],"images.":[37],"Traditional":[38],"clinical":[39,214],"approaches":[40],"are":[41,65,163],"often":[42],"time-consuming,":[43],"earlier":[45],"texture":[46,105],"attention":[47,116],"models":[48,198],"may":[49],"struggle":[50],"to":[51,108],"accurately":[52],"detect":[53],"subtle":[54],"features,":[55],"such":[56],"as":[57],"microaneurysms":[58],"or":[59],"abnormal":[60],"blood":[61],"vessel":[62],"patterns,":[63],"which":[64,100,126],"crucial":[66],"for":[67,82,209],"early":[68],"diagnosis.":[69],"To":[70],"address":[71],"this,":[72],"we":[73],"proposed":[74],"the":[75,119,145,171,200],"STMFNet":[76,193],"model,":[77],"hierarchical":[79],"framework":[80],"designed":[81],"classifying":[83],"11":[84,175],"stages":[85,177],"severity.":[88],"The":[89,95],"model":[90,146],"combines":[91],"two":[92],"primary":[93],"mechanisms:":[94],"Texture":[96],"Spatial":[97],"Attention":[98],"Network,":[99],"focuses":[101],"identifying":[103],"critical":[104],"features":[106,138,162],"related":[107],"while":[110],"minimizing":[111],"irrelevant":[112],"background":[113],"information":[114],"through":[115,166,180],"gating,":[117],"EfficientNet":[120],"backbone":[121],"with":[122],"Multi-Scale":[123],"Feature":[124],"Fusion,":[125],"captures":[127],"wide":[129],"range":[130],"image":[132],"patterns.":[133],"By":[134],"extracting":[135],"fusing":[137],"different":[140],"layers":[141],"EfficientNet-V1":[143],"BO,":[144],"effectively":[147],"learns":[148],"both":[149],"low":[150],"(e.g.,":[151,156],"edges,":[152],"blobs)":[153],"high":[155],"objects,":[157],"patterns)":[158],"level":[159],"representations.":[160],"These":[161],"further":[164],"refined":[165],"spatial":[167],"multi-scale":[168],"attention,":[169],"final":[172],"classification":[173],"into":[174],"achieved":[179],"Fully":[182],"Connected":[183],"Network":[184],"(FCN)":[185],"SoftMax.":[187],"Our":[188],"experimental":[189],"results":[190],"show":[191],"significantly":[194],"outperforms":[195],"existing":[196],"state-of-the-art":[197],"publicly":[201],"available":[202],"Kaggle":[203],"dataset,":[205],"demonstrating":[206],"its":[207],"potential":[208],"reliable":[210],"diagnosis":[212],"in":[213],"settings.":[215]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
