Classification and Segmentation of Periodontal Cyst for Digital Dental Diagnosis Using Deep Learning

Lakshmi T.K.; J. Dheeba

doi:10.24423/cames.505

Lakshmi T.K. Vellore Institute of Technology, Vellore, India
J. Dheeba Vellore Institute of Technology, Vellore, India

DOI: http://dx.doi.org/10.24423/cames.505

Abstract

The digital revolution is changing every aspect of life by simulating the ways humans think, learn and make decisions. Dentistry is one of the major fields where subsets of artificial intelligence are extensively used for disease predictions. Periodontitis, the most prevalent oral disease, is the main focus of this study. We propose methods for classifying and segmenting periodontal cysts on dental radiographs using CNN, VGG16, and U-Net. Accuracy of 77.78% is obtained using CNN, and enhanced accuracy of 98.48% is obtained through transfer learning with VGG16. The U-Net model also gives encouraging results. This study presents promising results, and in the future, the work can be extended with other pre-trained models and compared. Researchers working in this field can develop novel methods and approaches to support dental practitioners and periodontists in decisionmaking and diagnosis and use artificial intelligence to bridge the gap between humans and machines.

Keywords

CNN, classification, dental radiographs, deep learning, health care, machine transfer learning, periodontal cyst, predictive analytics, segmentation, U-Net, VGG16,

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