Comparison Analysis of CNN, SVC and Random Forest Algorithms in Segmentation of Teeth X-Ray Images
DOI:
https://doi.org/10.51983/ajcst-2022.11.1.3283Keywords:
Teeth X-Rays, CNN, SVC, Random Forest AlgorithmAbstract
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.
References
H. Ankarali, S. Ataoglu, S. Ankarali, and H. Guclu, "Pain Threshold, Pain Severity and Sensory Effects of Pain in Fibromyalgia Syndrome Patients: A new scale study," Bangladesh J. Med. Sci., Vol. 17, pp. 342-350, 2018.
Y. Y. Amer and M. J. Aqel, "An efficient segmentation algorithm for panoramic dental images," Procedia Computer Science, Vol. 65, pp. 718-725, 2015.
C. W. Wang, C. T. Huang, J. H. Lee, C. H. Li, S. W. Chang, M. J. Siao, T. M. Lai, B. Ibragimov, T. Vrtovec, O. Ronneberger, P. Fischer, T. F. Cootes, and C. Lindner, "A benchmark for comparison of dental radiography analysis algorithms," Medical Image Analysis, Vol. 31, pp. 63-76, 2016.
Roberto Lloréns, Valery Naranjo, Fernando López, Mariano Alcañiz, "Jaw tissues segmentation in dental 3D CT images using fuzzy-connectedness and morphological processing," Computer Methods and Programs in Biomedicine, Vol. 108, No. 2, pp. 832-843, 2012.
G. Jader, J. Fontineli, M. Ruiz, K. Abdalla, M. Pithon, and L. Oliveira, "Deep instance segmentation of teeth in panoramic x-ray images," in Conference on Graphics, Patterns and Images, IEEE, pp. 400-407, 2018.
V. T. N. Ngoc, A. C. Agwu, L. H. Son, T. M. Tuan, C. Nguyen Giap, M. T. G. Thanh, H. B. Duy and T. T. Ngan, "The Combination of Adaptive Convolutional Neural Network and Bag of Visual Words in Automatic Diagnosis of Third Molar Complications on Dental X-Ray Images," Diagnostics, Vol. 10, pp. 209, 2020. [Online]. Available: https://doi.org/10.3390/diagnostics10040209
Abdolvahab Ehsani Rad, Mohd Shafry Mohd Rahim, Rosely Kumoi and Alireza Norouzi, "Dental x-ray image segmentation and multiple feature extraction," 2nd World Conference on Innovation and Computer Sciences, 2012.
Y. Nishitani, R. Nakayama and D. Hayashi, et al., "Segmentation of teeth in panoramic dental X-ray images using U-Net with a loss function weighted on the tooth edge," Radiol Phys Technology, Vol. 14, pp. 64-69, 2021. [Online]. Available: https://doi.org/10.1007/s12 194-020-00603-1
P. L. Lin, P. Y. Huang, P. W. Huang, H. C. Hsu and C. C. Chen, "Teeth segmentation of dental periapical radiographs based on local singularity analysis," Computer Methods and Programs in Biomedicine, Vol. 113, No. 2, pp. 433-445, 2014. DOI: 10.1016/j. cmpb.2013.10.015.
B. Silva, L. Pinheiro, L. Oliveira and M. Pithon, "A study on tooth segmentation and numbering using end-to-end deep neural networks," 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 164-171, 2020, DOI: 10.1109/SIBGRAPI51 738. 2020.00030.
D. Tuzoff, L. Tuzova, M. Bornstein, A. Krasnov, M. Kharchenko, S. Nikolenko, M. Sveshnikov, and G. Bednenko, "Tooth detection and numbering in panoramic radiographs using convolutional neural networks," Dentomaxillofacial Radiology, Vol. 48, No. 4, 2019.
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