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- Title
Automatic Classification System for Periapical Lesions in Cone-Beam Computed Tomography.
- Authors
Calazans, Maria Alice Andrade; Ferreira, Felipe Alberto B. S.; Alcoforado, Maria de Lourdes Melo Guedes; Santos, Andrezza dos; Pontual, Andréa dos Anjos; Madeiro, Francisco
- Abstract
Imaging examinations are of remarkable importance for diagnostic support in Dentistry. Imaging techniques allow analysis of dental and maxillofacial tissues (e.g., bone, dentine, and enamel) that are inaccessible through clinical examination, which aids in the diagnosis of diseases as well as treatment planning. The analysis of imaging exams is not trivial; so, it is usually performed by oral and maxillofacial radiologists. The increasing demand for imaging examinations motivates the development of an automatic classification system for diagnostic support, as proposed in this paper, in which we aim to classify teeth as healthy or with endodontic lesion. The classification system was developed based on a Siamese Network combined with the use of convolutional neural networks with transfer learning for VGG-16 and DenseNet-121 networks. For this purpose, a database with 1000 sagittal and coronal sections of cone-beam CT scans was used. The results in terms of accuracy, recall, precision, specificity, and F1-score show that the proposed system has a satisfactory classification performance. The innovative automatic classification system led to an accuracy of about 70%. The work is pioneer since, to the authors knowledge, no other previous work has used a Siamese Network for the purpose of classifying teeth as healthy or with endodontic lesion, based on cone-beam computed tomography images.
- Subjects
AUTOMATIC classification; CONE beam computed tomography; AMELOBLASTS; ARTIFICIAL neural networks; CONVOLUTIONAL neural networks; IMAGE analysis; BESSEL beams
- Publication
Sensors (14248220), 2022, Vol 22, Issue 17, p6481
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s22176481