Publication: Temporomandibular Joint Disorders Multi-Class Classification Using Deep Learning
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Issued Date
2024-01-25
Resource Type
ISSN
09269630
eISSN
18798365
Scopus ID
2-s2.0-85183574923
Pubmed ID
38269713
Journal Title
Studies in Health Technology and Informatics
Volume
310
Start Page
1495
End Page
1496
Rights Holder(s)
SCOPUS
Bibliographic Citation
Studies in Health Technology and Informatics Vol.310 (2024) , 1495-1496
Suggested Citation
Thanathornwong B., Treebupachatsakul T., Teechot T., Poomrittigul S., Warin K., Suebnukarn S. Temporomandibular Joint Disorders Multi-Class Classification Using Deep Learning. Studies in Health Technology and Informatics Vol.310 (2024) , 1495-1496. 1496. doi:10.3233/SHTI231261 Retrieved from: https://hdl.handle.net/20.500.14740/20282
Corresponding Author(s)
Other Contributor(s)
Abstract
Temporomandibular joint (TMJ) disorders have been misinterpreted by various normal TMJ features leading to treatment failure. This study assessed deep learning algorithms, DenseNet-121 and InceptionV3, for multi-class classification of TMJ normal variations and disorders in 1,710 panoramic radiographs. The overall accuracy of DenseNet-121 and InceptionV3 were 0.99 and 0.95, respectively. The AUC from 0.99 to 1.00, indicating high performance for TMJ disorders classification in panoramic radiographs.