{"id":"https://openalex.org/W7204469167","doi":"https://doi.org/10.1201/9781003585268-6","title":"Deep learning approach for the effective diagnosis of wound using thermal imaging and computational models","display_name":"Deep learning approach for the effective diagnosis of wound using thermal imaging and computational models","publication_year":2026,"publication_date":"2026-08-27","ids":{"openalex":"https://openalex.org/W7204469167","doi":"https://doi.org/10.1201/9781003585268-6"},"language":"en","primary_location":{"id":"doi:10.1201/9781003585268-6","doi":"https://doi.org/10.1201/9781003585268-6","is_oa":false,"landing_page_url":"https://doi.org/10.1201/9781003585268-6","pdf_url":null,"source":{"id":"https://openalex.org/S4306463075","display_name":"CRC Press eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320584","host_organization_name":"CRC Press","host_organization_lineage":["https://openalex.org/P4310320584","https://openalex.org/P4310320547"],"host_organization_lineage_names":["CRC Press","Taylor & Francis"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Thermal Imaging and Computational Intelligence Techniques in Disease Detection","raw_type":"book-chapter"},"volume":{"id":"https://openalex.org/W7204442407","display_name":"Thermal Imaging and Computational Intelligence Techniques in Disease Detection"},"type":"book-chapter","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/A5063942248","display_name":"Shiva Sharma","orcid":"https://orcid.org/0000-0003-1384-8679","observed_orcids":["https://orcid.org/0000-0003-1384-8679"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shiva Sharma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136779659","display_name":"Sudheesh K. Shukla","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sudheesh K. Shukla","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055335117","display_name":"Krishna Kuben Govender","orcid":"https://orcid.org/0000-0002-9058-1675","observed_orcids":["https://orcid.org/0000-0002-9058-1675"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krishna K. Govender","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5112581217","display_name":"Penny Poomani Govender","orcid":"https://orcid.org/0000-0001-6639-3829","observed_orcids":["https://orcid.org/0000-0001-6639-3829"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Penny P. Govender","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"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":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"112","last_page":"128"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12994","display_name":"Infrared Thermography in Medicine","score":0.9994999766349792,"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/T12994","display_name":"Infrared Thermography in Medicine","score":0.9994999766349792,"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/T10771","display_name":"Wound Healing and Treatments","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T11856","display_name":"Thermography and Photoacoustic Techniques","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/wound-diagnosis","display_name":"wound diagnosis","score":0.9440000057220459},{"id":"https://openalex.org/keywords/infrared-thermography","display_name":"infrared thermography","score":0.9549999833106995},{"id":"https://openalex.org/keywords/multimodal-fusion","display_name":"multimodal fusion","score":0.8629999756813049},{"id":"https://openalex.org/keywords/early-fusion","display_name":"feature-level fusion","score":0.9089999794960022},{"id":"https://openalex.org/keywords/decision-level-fusion","display_name":"decision fusion","score":0.9990000128746033},{"id":"https://openalex.org/keywords/burn-depth-classification","display_name":"burn depth classification","score":0.8240000009536743},{"id":"https://openalex.org/keywords/wound-infection-detection","display_name":"wound infection detection","score":0.9380000233650208},{"id":"https://openalex.org/keywords/wound-healing-assessment","display_name":"wound healing assessment","score":0.7020000219345093},{"id":"https://openalex.org/keywords/computational-modeling","display_name":"computational modeling","score":0.4860000014305115},{"id":"https://openalex.org/keywords/clinical-metadata","display_name":"clinical metadata","score":0.45399999618530273}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7480999827384949},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.652400016784668},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5608999729156494},{"id":"https://openalex.org/C2779222261","wikidata":"https://www.wikidata.org/wiki/Q624587","display_name":"Thermography","level":3,"score":0.5608000159263611},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.554099977016449},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5491999983787537},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.49959999322891235},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.4584999978542328},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3212999999523163},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C2993873987","wikidata":"https://www.wikidata.org/wiki/Q184753","display_name":"Wound closure","level":3,"score":0.2529999911785126},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1201/9781003585268-6","doi":"https://doi.org/10.1201/9781003585268-6","is_oa":false,"landing_page_url":"https://doi.org/10.1201/9781003585268-6","pdf_url":null,"source":{"id":"https://openalex.org/S4306463075","display_name":"CRC Press eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320584","host_organization_name":"CRC Press","host_organization_lineage":["https://openalex.org/P4310320584","https://openalex.org/P4310320547"],"host_organization_lineage_names":["CRC Press","Taylor & Francis"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Thermal Imaging and Computational Intelligence Techniques in Disease Detection","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9391000270843506,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"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/W7131423681","https://openalex.org/W7160662523","https://openalex.org/W4391462245","https://openalex.org/W7223222090","https://openalex.org/W7151974751","https://openalex.org/W7163813764","https://openalex.org/W4406319023","https://openalex.org/W4407470434","https://openalex.org/W4288391511","https://openalex.org/W4413221068"],"abstract_inverted_index":{"The":[0],"accurate":[1,97],"and":[2,16,31,55,63,81,95,101,109,129,153,169,179],"timely":[3],"diagnosis":[4],"of":[5,78,90,99,147,150,160,188],"wounds":[6],"remains":[7],"a":[8,75],"clinical":[9,64],"challenge,":[10],"particularly":[11],"in":[12,38,166,175],"assessing":[13],"depth,":[14],"severity,":[15],"healing":[17],"progression.":[18],"This":[19,84],"study":[20],"proposes":[21],"an":[22],"integrated":[23],"deep":[24],"learning":[25],"framework":[26],"that":[27],"leverages":[28],"thermal":[29,45,59,128],"imaging":[30],"computational":[32],"modeling":[33],"to":[34,113,137,184],"enhance":[35],"diagnostic":[36],"precision":[37],"wound":[39,71,151,190],"assessment.":[40],"Infrared":[41],"thermography":[42],"captures":[43,124],"real-time":[44],"patterns":[46],"reflecting":[47],"underlying":[48],"physiological":[49],"changes":[50],"such":[51],"as":[52],"inflammation,":[53],"infection,":[54],"perfusion":[56],"anomalies.":[57],"Integrating":[58],"imaging,":[60],"RGB":[61],"images,":[62],"metadata":[65],"significantly":[66],"enhances":[67],"artificial":[68],"intelligence":[69],"(AI)-driven":[70],"diagnostics":[72],"by":[73,119],"providing":[74],"comprehensive":[76],"view":[77],"physiological,":[79],"visual,":[80],"patient-specific":[82],"factors.":[83],"multimodal":[85,161],"approach":[86],"improves":[87],"the":[88,115,158,186],"sensitivity":[89],"AI":[91,105],"models,":[92],"enabling":[93],"earlier":[94],"more":[96],"detection":[98,146],"complications":[100],"facilitating":[102],"personalized":[103,154],"assessments.":[104],"models":[106],"employ":[107],"feature-level":[108],"decision-level":[110,133],"fusion":[111,123,134],"strategies":[112],"leverage":[114],"unique":[116],"information":[117],"provided":[118],"each":[120],"modality.":[121],"Feature-level":[122],"intricate":[125],"correlations":[126],"between":[127],"visual":[130],"cues,":[131],"while":[132],"offers":[135],"robustness":[136],"missing":[138],"or":[139],"noisy":[140],"data.":[141],"Clinical":[142],"applications":[143],"include":[144],"early":[145],"infections,":[148],"differentiation":[149],"pathologies,":[152],"prognostics.":[155],"Studies":[156],"validate":[157],"effectiveness":[159],"systems,":[162],"achieving":[163],"high":[164],"accuracy":[165],"referral":[167],"decisions":[168],"burn":[170],"depth":[171],"classification.":[172],"Challenges":[173],"remain":[174],"data":[176],"alignment,":[177],"interpretability,":[178],"scalability,":[180],"necessitating":[181],"interdisciplinary":[182],"collaboration":[183],"realize":[185],"potential":[187],"AI-enhanced":[189],"carefully.":[191]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2026-08-28T00:00:00"}
