{"id":"https://openalex.org/W7202201582","doi":"https://doi.org/10.1109/siu71813.2026.11637045","title":"An AI-Driven Hybrid Framework for Cephalometric Landmark Detection via Metaheuristic-Optimized Heatmap CNN and Stationary Wavelet Transform","display_name":"An AI-Driven Hybrid Framework for Cephalometric Landmark Detection via Metaheuristic-Optimized Heatmap CNN and Stationary Wavelet Transform","publication_year":2026,"publication_date":"2026-07-01","ids":{"openalex":"https://openalex.org/W7202201582","doi":"https://doi.org/10.1109/siu71813.2026.11637045"},"language":"en","primary_location":{"id":"doi:10.1109/siu71813.2026.11637045","doi":"https://doi.org/10.1109/siu71813.2026.11637045","is_oa":true,"landing_page_url":"https://doi.org/10.1109/siu71813.2026.11637045","pdf_url":null,"source":{"id":"https://openalex.org/S4363607818","display_name":"Signal Processing and Communications Applications Conference (SIU)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 34th Signal Processing and Communications Applications Conference (SIU)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1109/siu71813.2026.11637045","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031434019","display_name":"Yusuf Bahri \u00d6z\u00e7elik","orcid":"https://orcid.org/0000-0001-6326-9398","observed_orcids":["https://orcid.org/0000-0001-6326-9398"]},"institutions":[{"id":"https://openalex.org/I3017902709","display_name":"Zonguldak B\u00fclent Ecevit University","ror":"https://ror.org/01dvabv26","country_code":"TR","type":"education","lineage":["https://openalex.org/I3017902709"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Yusuf Bahri \u00d6z\u00e7elik","raw_affiliation_strings":["Zonguldak B\u00fclent Ecevit \u00dcniversitesi"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zonguldak B\u00fclent Ecevit \u00dcniversitesi","institution_ids":["https://openalex.org/I3017902709"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004941306","display_name":"Ayta\u00e7 Altan","orcid":"https://orcid.org/0000-0001-7923-4528","observed_orcids":["https://orcid.org/0000-0001-7923-4528"]},"institutions":[{"id":"https://openalex.org/I3017902709","display_name":"Zonguldak B\u00fclent Ecevit University","ror":"https://ror.org/01dvabv26","country_code":"TR","type":"education","lineage":["https://openalex.org/I3017902709"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ayta\u00e7 Altan","raw_affiliation_strings":["Zonguldak B\u00fclent Ecevit \u00dcniversitesi"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zonguldak B\u00fclent Ecevit \u00dcniversitesi","institution_ids":["https://openalex.org/I3017902709"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056895568","display_name":"Orhan \u00c7i\u00e7ek","orcid":"https://orcid.org/0000-0002-8172-6043","observed_orcids":["https://orcid.org/0000-0002-8172-6043"]},"institutions":[{"id":"https://openalex.org/I3017902709","display_name":"Zonguldak B\u00fclent Ecevit University","ror":"https://ror.org/01dvabv26","country_code":"TR","type":"education","lineage":["https://openalex.org/I3017902709"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Orhan \u00c7i\u00e7ek","raw_affiliation_strings":["Zonguldak B\u00fclent Ecevit \u00dcniversitesi"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zonguldak B\u00fclent Ecevit \u00dcniversitesi","institution_ids":["https://openalex.org/I3017902709"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3017902709"],"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":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11363","display_name":"Dental Radiography and Imaging","score":0.9463000297546387,"subfield":{"id":"https://openalex.org/subfields/3504","display_name":"Oral Surgery"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11363","display_name":"Dental Radiography and Imaging","score":0.9463000297546387,"subfield":{"id":"https://openalex.org/subfields/3504","display_name":"Oral Surgery"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10356","display_name":"Orthodontics and Dentofacial Orthopedics","score":0.04129999876022339,"subfield":{"id":"https://openalex.org/subfields/3505","display_name":"Orthodontics"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.005200000014156103,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cephalometric-landmark-detection","display_name":"cephalometric landmark detection","score":0.9390000104904175},{"id":"https://openalex.org/keywords/skeletal-landmarks","display_name":"skeletal landmarks","score":0.8880000114440918},{"id":"https://openalex.org/keywords/lateral-cephalogram","display_name":"lateral cephalometric radiographs","score":0.9380000233650208},{"id":"https://openalex.org/keywords/yolo11","display_name":"YOLOv11","score":0.9319999814033508},{"id":"https://openalex.org/keywords/vitpose","display_name":"ViTPose","score":0.8870000243186951},{"id":"https://openalex.org/keywords/stationary-wavelet-transform","display_name":"stationary wavelet transform","score":0.996999979019165},{"id":"https://openalex.org/keywords/metaheuristic-hyperparameter-optimization","display_name":"metaheuristic hyperparameter optimization","score":0.8889999985694885},{"id":"https://openalex.org/keywords/orthodontic-diagnosis","display_name":"orthodontic diagnosis","score":0.8190000057220459},{"id":"https://openalex.org/keywords/landmark-detection","display_name":"landmark detection","score":0.4390000104904175}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7203999757766724},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.65829998254776},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6064000129699707},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.599399983882904},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5076000094413757},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4569000005722046},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.44269999861717224},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3303999900817871},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26330000162124634},{"id":"https://openalex.org/C95722684","wikidata":"https://www.wikidata.org/wiki/Q2622756","display_name":"Continuous wavelet transform","level":5,"score":0.2621000111103058}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/siu71813.2026.11637045","doi":"https://doi.org/10.1109/siu71813.2026.11637045","is_oa":true,"landing_page_url":"https://doi.org/10.1109/siu71813.2026.11637045","pdf_url":null,"source":{"id":"https://openalex.org/S4363607818","display_name":"Signal Processing and Communications Applications Conference (SIU)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 34th Signal Processing and Communications Applications Conference (SIU)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1109/siu71813.2026.11637045","doi":"https://doi.org/10.1109/siu71813.2026.11637045","is_oa":true,"landing_page_url":"https://doi.org/10.1109/siu71813.2026.11637045","pdf_url":null,"source":{"id":"https://openalex.org/S4363607818","display_name":"Signal Processing and Communications Applications Conference (SIU)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 34th Signal Processing and Communications Applications Conference (SIU)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.428600013256073,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"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/W2765894129","https://openalex.org/W7221187194","https://openalex.org/W1226347839","https://openalex.org/W7125434250","https://openalex.org/W7221131597","https://openalex.org/W4406702842","https://openalex.org/W2980749260","https://openalex.org/W3194199857","https://openalex.org/W2979306609","https://openalex.org/W213139441"],"abstract_inverted_index":{"High-precision":[0],"detection":[1,91],"of":[2,10,62,104],"cephalometric":[3,109],"skeletal":[4],"landmarks":[5],"(CSLs)":[6],"enables":[7],"accurate":[8],"analysis":[9],"skeletal,":[11],"dental,":[12],"and":[13,22,42,59,87],"soft-tissue":[14],"relationships,":[15],"which":[16],"is":[17],"essential":[18],"for":[19,49,108],"orthodontic":[20],"diagnosis":[21],"treatment":[23],"planning.":[24],"In":[25],"this":[26],"study,":[27],"regional":[28],"inference":[29],"using":[30],"YOLOv11":[31],"was":[32,47],"employed":[33],"to":[34],"automatically":[35],"detect":[36],"17":[37],"CSLs":[38],"on":[39],"lateral":[40],"cephalograms,":[41],"a":[43],"heatmap-based":[44],"ViTPose":[45,64],"model":[46,65,107],"developed":[48],"each":[50],"landmark.":[51],"To":[52],"enhance":[53],"localization":[54,77],"performance,":[55,78],"stationary":[56],"wavelet":[57],"transform":[58],"hyperparameter":[60],"optimization":[61],"the":[63,72,80,89,102,105],"were":[66],"used.":[67],"The":[68],"results":[69],"show":[70],"that":[71],"proposed":[73,106],"approach":[74],"improves":[75],"CSL":[76],"reducing":[79],"mean":[81],"radial":[82],"error":[83,98],"by":[84,93],"approximately":[85],"19.75%":[86],"increasing":[88],"success":[90],"rate":[92],"about":[94],"5%":[95],"across":[96],"all":[97],"thresholds.":[99],"This":[100],"highlights":[101],"suitability":[103],"analysis.":[110]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-08-12T00:00:00"}