{"id":"https://openalex.org/W4412151924","doi":"https://doi.org/10.32604/cmc.2025.065853","title":"Efficient Wound Classification Using YOLO11n: A Lightweight Deep Learning Approach","display_name":"Efficient Wound Classification Using YOLO11n: A Lightweight Deep Learning Approach","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412151924","doi":"https://doi.org/10.32604/cmc.2025.065853"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.065853","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.065853","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.065853","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068189742","display_name":"Fathe Jeribi","orcid":"https://orcid.org/0000-0002-8511-8002"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fathe Jeribi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076822040","display_name":"Ayesha Siddiqa","orcid":"https://orcid.org/0000-0003-0780-6376"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ayesha Siddiqa","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038410086","display_name":"Hareem Kibriya","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hareem Kibriya","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000656537","display_name":"Ali Tahir","orcid":"https://orcid.org/0000-0003-2914-019X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ali Tahir","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5066836183","display_name":"Nadim Rana","orcid":"https://orcid.org/0000-0002-6215-4414"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nadim Rana","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5068189742"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3717,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.83995538,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"85","issue":"1","first_page":"955","last_page":"982"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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/T11670","display_name":"Pressure Ulcer Prevention and Management","score":0.9254000186920166,"subfield":{"id":"https://openalex.org/subfields/3609","display_name":"Occupational Therapy"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5498077273368835},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5366653203964233},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5341683030128479}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5498077273368835},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5366653203964233},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5341683030128479}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.065853","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.065853","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.065853","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.065853","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2914065803","https://openalex.org/W2954074628","https://openalex.org/W2964274014","https://openalex.org/W2967307920","https://openalex.org/W3092760571","https://openalex.org/W3200693321","https://openalex.org/W4312864984","https://openalex.org/W4313894869","https://openalex.org/W4367189201","https://openalex.org/W4387909199","https://openalex.org/W4390430704","https://openalex.org/W4393143202","https://openalex.org/W4393388407","https://openalex.org/W4401486111","https://openalex.org/W4401697900","https://openalex.org/W4401833387","https://openalex.org/W4408349403"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W3086377361"],"abstract_inverted_index":{"Wound":[0],"classification":[1,50,170,182,193],"is":[2,95,108,247],"a":[3,43,148,186],"critical":[4],"task":[5],"in":[6,28,89,97,234,257],"healthcare,":[7],"requiring":[8],"accurate":[9],"and":[10,40,71,84,116,132,181,201,214,220],"efficient":[11,87,139],"diagnostic":[12],"tools":[13],"to":[14,126,166,249,253],"support":[15],"clinicians.":[16],"In":[17],"this":[18],"paper,":[19],"we":[20],"investigated":[21],"the":[22,25,38,52,106,123,152,205,210,231,238,250],"effectiveness":[23],"of":[24,32,42,102,105,114,189,218],"YOLO11n":[26,45,190,208,251],"model":[27,46,107,252],"classifying":[29,223,235],"different":[30,103],"types":[31],"wound":[33,49,58,169,192,258],"images.":[34,259],"This":[35,163],"study":[36],"presents":[37],"training":[39,115],"evaluation":[41,188],"lightweight":[44,136],"for":[47,138,159,191,222],"automated":[48,168],"using":[51,204,237],"AZH":[53,206,239],"dataset,":[54],"which":[55],"includes":[56],"six":[57,226],"classes:":[59],"Background":[60],"(BG),":[61],"Normal":[62],"Skin":[63],"(N),":[64],"Diabetic":[65],"(D),":[66],"Pressure":[67],"(P),":[68],"Surgical":[69],"(S),":[70],"Venous":[72],"(V).":[73],"The":[74,92,99,111,141],"model\u2019s":[75,93,124,153],"architecture,":[76],"optimized":[77],"through":[78,171],"experiments":[79,121],"with":[80,129],"varying":[81],"batch":[82],"sizes":[83],"epochs,":[85],"ensures":[86],"deployment":[88],"resource-constrained":[90],"environments.":[91],"architecture":[94,137],"discussed":[96],"detail.":[98],"visual":[100,112],"representation":[101],"blocks":[104],"also":[109],"presented.":[110],"results":[113],"validation":[117],"are":[118],"shown.":[119],"Our":[120],"emphasize":[122],"ability":[125],"classify":[127],"wounds":[128,224,236],"high":[130],"precision":[131],"recall,":[133],"leveraging":[134],"its":[135],"computation.":[140],"findings":[142],"demonstrate":[143],"that":[144],"fine-tuning":[145],"hyperparameters":[146],"has":[147],"significant":[149],"impact":[150],"on":[151],"detection":[154],"performance,":[155],"making":[156],"it":[157],"suitable":[158],"real-world":[160],"medical":[161],"applications.":[162],"research":[164],"contributes":[165],"advancing":[167],"deep":[172],"learning,":[173],"while":[174],"addressing":[175],"challenges":[176],"such":[177],"as":[178],"dataset":[179],"imbalance":[180],"intricacies.":[183],"We":[184],"conducted":[185],"comprehensive":[187],"across":[194],"multiple":[195],"configurations,":[196],"including":[197],"6,":[198],"5,":[199],"4,":[200],"3-way":[202],"classification,":[203],"dataset.":[207,240],"acquires":[209],"highest":[211],"F1":[212],"score":[213],"mean":[215],"Average":[216],"Precision":[217],"0.836":[219],"0.893":[221],"into":[225],"classes,":[227],"respectively.":[228],"It":[229],"outperforms":[230],"existing":[232],"methods":[233],"Moreover,":[241],"Gradient-weighted":[242],"Class":[243],"Activation":[244],"Mapping":[245],"(Grad-CAM)":[246],"applied":[248],"visualize":[254],"class-relevant":[255],"regions":[256]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}