Home / Current Issue / Paper 1705140
A Novel Approach for Dental Caries Classification Using Transfer Learning
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
Transfer learning has become a significant field of research in radiology. Periapical and panoramic radiographs have long been utilized by dental professionals to assist in the identification of the majority of dental problems. Dental practitioners usually treat tooth decay, also known as caries, physically based on the photographs they obtain from dental labs. To help with the enormous labor load on the healthcare society and for more precision, machine learning aids various smart vision systems based on computer applications. It also has better market demand. In order to provide novel techniques for fully automated tooth caries detection, this study has provided a framework for the recognition and evaluation of dental caries. The methods will make use of categorization and transfer learning techniques. First, using the provided OPG image as input, the suggested system will be able to identify the affected area of the tooth where caries are present. It will be possible to determine whether or not there is caries in the tooth after identifying the area that appears to be diseased. In addition to improving precision and accuracy, this method will also save up the radiologist's time. Dental cavities will be detectable by the X-ray machines, and the findings can be sent directly to the dentist for additional diagnostics.
Keywords
Dental caries, transfer learning, classification, Convolutional neural network, panoramic images, MI-DCNN (multi-input convolutional neural network), VGG16, ResNet50, MobileNet, Dataset, Comparison, OPG (oorthopantomography)
References
[1] A. Imak, A. Celebi, K. Siddique, M. Turkoglu, A. Sengur and I. Salam, "Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network," in IEEE Access, vol. 10, pp. 18320-18329, 2022, 10.1109/ACCESS.2022.3150358.
[2] H. Yu, Z. Lin, Y. Liu, J. Su, B. Chen and G. Lu, "A New Technique for Diagnosis of Dental Caries on the Children’s First Permanent Molar," in IEEE Access, vol. 8, pp. 185776-185785, 2020,
[3] Lian, Luya & Zhu, Tianer & Zhu, Fudong & Zhu, Haihua. (2021). Deep Learning for Caries Detection and Classification. Diagnostics. 11. 1672. 10.3390/diagnostics11091672.
[4] G. F. Olsen, S. S. Brilliant, D. Primeaux and K. Najarian, "An image-processing enabled dental caries detection system," 2009 ICME International Conference on Complex Medical Engineering, 2009, pp. 1 -8, 10.1109/ICCME.2009.4906674.
[5] M.A. Hafeez Khan, Prasad S. Giri, J. Angel Arul Jothi, "Detection of Cavities from Oral Images using Convolutional Neural Networks", 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), pp.1-6, 2022.
[6] Mao, Y.-C.; Chen, T.-Y.; Chou, H.-S.; Lin, S.-Y.; Liu, S.-Y.;Chen, Y.-A.; Liu, Y.-L.; Chen, C.- A.;Huang, Y.-C.; Chen, S.-L.; et al. Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs. Sensors 2021,21, 4613.
[7] Lian, L.; Zhu, T.; Zhu, F.; Zhu, H. Deep Learning for Caries Detection and Classification. Diagnostics 2021,11, 1672.
[8] Fung MHT, Wong MCM, Lo ECM, CH Chu (2013) Early Childhood Caries: A Literature Review. Oral Hyg Health 1: 107.
[9] R. Obuchowicz , K. Nurzynska, B. Obuchowicz, “Caries detection enhancement using texture feature maps of intraoral radiographs,” Oral radiol., vol. 36 no. 3, pp. 275-287, Jul. 2020 10.1007/s1182-018-0354-8
[10] L. Megalan Leo and T. Kalapalatha Reddy, ‘‘Learning compact and discriminative hybrid neural network for dental caries classification,’’ Microprocessors Microsyst., vol. 82, Apr. 2021, Art. no. 103836, 10.1016/j.micpro.2021.103836
[11] H. Yang, E. Jo, H. J. Kim, I.-H. Cha, Y.-S. Jung, W. Nam, J.-Y. Kim, J.-K. Kim, Y. H. Kim, T. G. Oh, S.-S. Han, H. Kim, and D. Kim, ‘‘Deep learning for automated detection of cyst and tumors of the jaw in panoramic radiographs,’’ J. Clin. Med., vol. 9, no. 6, p. 1839, Jun. 2020, 10.3390/jcm9061839
[12] O. Kwon, T. H. Yong, S. R. Kang, J. E. Kim, K. H. Huh, M. S. Heo, S. S. Lee, S. C. Choi, and W. J. Yi, ‘‘Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network,’’ Dentomaxillofacial Radiol., vol. 49, no. 8, Jul. 2020, Art. no. 20200185, 10.1259/dmfr.20200185
[13] Y. P. Huang and S. Y. Lee, ‘‘Deep learning for caries detection using optical coherence tomography,’’ medRxiv, early access, 10.1101/2021.05.04.21256502.
[14] J. Naam, J. Harlan, S. Madenda, and E. P. Wibowo, ‘‘Image processing of panoramic dental X-ray for identifying proximal caries,’’ Indonesian J. Elect. Eng. Comput. Sci. (Telkomnika), vol. 5, no. 2, pp. 702–708, Jun. 2017. [Online]. Available: https://pdfs.semanticscholar.org/7cd9/d2e1 ff9afbe0f84a40dc32ef77c91eeff0be.pdf, 12928/TELKOMNIKA. v15i2.4622
[15] S. Oprea, C. Marinescu, I. Lita, M. Jurianu, D. A. Visan, and I. B. Cioc, ‘‘Image processing techniques used for dental X-ray image analysis,’’ in Proc. 31st Int. Spring Seminar Electron. Technol., May 2008, pp. 125–129,
[16] S. K. Khare and V. Bajaj, ‘‘Time–frequency representation and convolutional neural network-based emotion recognition,’’ IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 7, pp. 2901–2909, Jul. 2021, 10.1109/TNNLS.2020.3008938
[17] P. Singh and P. Sehgal, ‘‘Automated caries detection based on radon transformation and DCT,’’ in Proc. 8th Int. Conf. Comput., Commun. Netw. Technol. (ICCCNT), Jul. 2017, pp. 1–6,
[18] O. E. Langland, R. P. Langlais, and J. W. Preece, Principles of Dental Imaging. Philadelphia, PA, USA: Lippincott Williams & Wilkins, 2002.
[4] S. C. White and M. J. Pharoah, Oral Radiology- E-Book: Principles and Interpretation. Amsterdam, The Netherlands: Elsevier, 2014.
[19] K. He, X. Zhang, S. Ren, and J. Sun, ‘‘Deep residual learning for image recognition,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 770–778. [Online]. Available:
[20] K. Simonyan and A. Zisserman, ‘‘Very deep convolutional networks for large-scale image recognition,’’ 2014, arXiv:1409.155.
How to cite this paper
@article{1705140,
author = {Trupti Uttamrao Ahirrao, Roshni Bhave},
title = {A Novel Approach for Dental Caries Classification Using Transfer Learning},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {7},
number = {4},
pages = {312-320},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705140.pdf},
abstract = {Transfer learning has become a significant field of research in radiology. Periapical and panoramic radiographs have long been utilized by dental professionals to assist in the identification of the majority of dental problems. Dental practitioners usually treat tooth decay, also known as caries, physically based on the photographs they obtain from dental labs. To help with the enormous labor load on the healthcare society and for more precision, machine learning aids various smart vision systems based on computer applications. It also has better market demand. In order to provide novel techniques for fully automated tooth caries detection, this study has provided a framework for the recognition and evaluation of dental caries. The methods will make use of categorization and transfer learning techniques. First, using the provided OPG image as input, the suggested system will be able to identify the affected area of the tooth where caries are present. It will be possible to determine whether or not there is caries in the tooth after identifying the area that appears to be diseased. In addition to improving precision and accuracy, this method will also save up the radiologist's time. Dental cavities will be detectable by the X-ray machines, and the findings can be sent directly to the dentist for additional diagnostics.},
keywords = {Dental caries, transfer learning, classification, Convolutional neural network, panoramic images, MI-DCNN (multi-input convolutional neural network), VGG16, ResNet50, MobileNet, Dataset, Comparison, OPG (oorthopantomography)},
month = {October},
}