Comparison Analysis of CNN, SVC and Random Forest Algorithms in Segmentation of Teeth X-Ray Images


  • G. C. Jyothi Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India
  • Chetana Prakash Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India
  • G. A. Babitha Department of Periodontics, College of Dental Science, Davangere, Karnataka, India
  • G. H. Kiran Kumar Department of Electronics and Communication, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India



Teeth X-Rays, CNN, SVC, Random Forest Algorithm


In dental diagnosis, rapid identification of dental complications from radiographs requires highly experienced medical professionals. Occasionally, depending exclusively on a expert's judgement could lead to changes in diagnosis, that could eventually lead to difficult treatment. Although fully automatic diagnostic tools aren’t still anticipated, image pattern recognition has grown into decision support, opening with discovery of teeth and its constituents on X-ray images. Dental discovery is a topic of study for more than previous two decades, depending primarily on threshold and region-based strategies. In this study, we proposed segmentation based Teeth X-Ray images using a couple of machine learning algorithms as well as deep learning algorithms i.e., Support Vector Classifier (SVC), Random Forest algorithm and Convolutional Neural network (CNN)  which would help us in accurate identification and classification. This article also presents a comprehensive comparison between these Algorithms.


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How to Cite

Jyothi, G. C., Prakash, C., Babitha, G. A., & Kiran Kumar, G. H. (2022). Comparison Analysis of CNN, SVC and Random Forest Algorithms in Segmentation of Teeth X-Ray Images. Asian Journal of Computer Science and Technology, 11(1), 40–47.