Dental X –Ray Images for Automated Detection of Caries Classes Using Deep Learning Techniques
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Abstract
Purpose: Dental caries can emerge anywhere in the mouth particularly in the interior of the cheeks and the gums. Some of the indications are patches on the inner lining of the mouth, along with bleeding, toothache, numbness and an unusual red and white staining. Hence, it is important to predict the presence of cavity at an early stage. The currently available manual method is inefficient and hence we provide an advanced method by using the deep learning concepts.
Materials and Methods: In this work, different types of algorithms such as Res Net, Deeper Google Net and mini VGG Net are to be used to predict the class of cavity at an early stage.
Results: A comparison between the accuracy of three different algorithms is given in this paper. Thus, by using efficient deep learning algorithms, it will be able to predict the presence of cavity and the class of cavity at an early stage and take necessary steps to overcome it.
Conclusion: In this work, a comparison between three different algorithms is given and proved that the efficient algorithm is the inception algorithm among the other algorithms and achieve an accuracy of about 98%, which is suitable for use in hospitals.
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24- Jiangchang Xu, Shiming Wang, Zijie Zhou, Jiannan Liu, Xiaoyi Jiang, and Xiaojun Chen, "Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net." International Journal of Computer Assisted Radiology and Surgery, Vol. 15pp. 1457-65, (2020).
25- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, "ImageNet classification with deep convolutional neural networks." Communications of the ACM, Vol. 60 (No. 6), pp. 84-90, (2017).
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28- K Vijayakumar, V Rajinikanth, and MK Kirubakaran, "Automatic detection of breast cancer in ultrasound images using Mayfly algorithm optimized handcrafted features." Journal of X-Ray Science and Technology, Vol. 30 (No. 4), pp. 751-66, (2022).
29- Ali B Syed and Adam C Zoga, "Artificial intelligence in radiology: current technology and future directions." in Seminars in musculoskeletal radiology, (2018), Vol. 22 (No. 05): Thieme medical publishers, pp. 540-45.
2- Hakan Çolak, Çoruh T Dülgergil, Mehmet Dalli, and Mehmet Mustafa Hamidi, "Early childhood caries update: A review of causes, diagnoses, and treatments." Journal of natural science, biology, and medicine, Vol. 4 (No. 1), p. 29, (2013).
3- Yoon Lee, "Diagnosis and prevention strategies for dental caries." Journal of lifestyle medicine, Vol. 3 (No. 2), p. 107, (2013).
4- Thomas J Hilton, Jack L Ferracane, James C Broome, José dos Santos, and James B Summitt, "Summitt's fundamentals of operative dentistry: a contemporary approach." (No Title), (2013).
5- Poul Erik Petersen, Denis Bourgeois, Hiroshi Ogawa, Saskia Estupinan-Day, and Charlotte Ndiaye, "The global burden of oral diseases and risks to oral health." Bulletin of the world health organization, Vol. 83pp. 661-69, (2005).
6- Opeyemi O Daramola, Carrie E Flanagan, Robert H Maisel, and Rick M Odland, "Diagnosis and treatment of deep neck space abscesses." Otolaryngology—Head and Neck Surgery, Vol. 141 (No. 1), pp. 123-30, (2009).
7- R Krithiga and C Lakshmi, "A survey: segmentation in dental X-ray images for diagnosis of dental caries." Int J Control Theory Appl, Vol. 9 (No. 40), p. 941, (2016).
8- Sudhir Kumar Sharma, K Vijayakumar, Vinod J Kadam, and Sheldon Williamson, "Breast cancer prediction from microRNA profiling using random subspace ensemble of LDA classifiers via Bayesian optimization." Multimedia Tools and Applications, Vol. 81 (No. 29), pp. 41785-805, (2022).
9- Anupama Bhan, Garima Vyas, Sourav Mishra, and Pulkit Pandey, "Detection and grading severity of caries in dental X-ray images." in 2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE), (2016): IEEE, pp. 375-78.
10- K Vijayakumar, K Pradeep Mohan Kumar, and Daniel Jesline, "Implementation of software agents and advanced AoA for disease data analysis." Journal of Medical Systems, Vol. 43pp. 1-6, (2019).
11- R Vinayakumar, KP Soman, and Prabaharan Poornachandran, "Applying convolutional neural network for network intrusion detection." in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), (2017): IEEE, pp. 1222-28.
12- Daniel S Kermany et al., "Identifying medical diagnoses and treatable diseases by image-based deep learning." cell, Vol. 172 (No. 5), pp. 1122-31. e9, (2018).
13- Janka Hatvani, Adrian Basarab, Jean-Yves Tourneret, Miklós Gyöngy, and Denis Kouamé, "A tensor factorization method for 3-D super resolution with application to dental CT." IEEE transactions on medical imaging, Vol. 38 (No. 6), pp. 1524-31, (2018).
14- Jonathan Long, Evan Shelhamer, and Trevor Darrell, "Fully convolutional networks for semantic segmentation." in Proceedings of the IEEE conference on computer vision and pattern recognition, (2015), pp. 3431-40.
15- Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla, "Segnet: A deep convolutional encoder-decoder architecture for image segmentation." IEEE transactions on pattern analysis and machine intelligence, Vol. 39 (No. 12), pp. 2481-95, (2017).
16- Olaf Ronneberger, Philipp Fischer, and Thomas Brox, "U-net: Convolutional networks for biomedical image segmentation." in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, (2015): Springer, pp. 234-41.
17- Aman Rana, Gregory Yauney, Lawrence C Wong, Otkrist Gupta, Ali Muftu, and Pratik Shah, "Automated segmentation of gingival diseases from oral images." in 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT), (2017): IEEE, pp. 144-47.
18- Sultan Imangaliyev, Monique H van der Veen, Catherine MC Volgenant, Bart JF Keijser, Wim Crielaard, and Evgeni Levin, "Deep learning for classification of dental plaque images." in Machine Learning, Optimization, and Big Data: Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016, Revised Selected Papers 2, (2016): Springer, pp. 407-10.
19- Ramzi Ben Ali, Ridha Ejbali, and Mourad Zaied, "Detection and classification of dental caries in x-ray images using deep neural networks." in International conference on software engineering advances (ICSEA), (2016), p. 236.
20- Sima Nikneshan, Sudeh Mohseni, Mahtab Nouri, Hoora Hadian, and Mohammad Javad Kharazifard, "The effect of emboss enhancement on reliability of landmark identification in digital lateral cephalometric images." Iranian Journal of Radiology, Vol. 12 (No. 2), (2015).
21- Jindan Zhou and Mohamed Abdel-Mottaleb, "A content-based system for human identification based on bitewing dental X-ray images." Pattern Recognition, Vol. 38 (No. 11), pp. 2132-42, (2005).
22- YH Lai and PL Lin, "Effective segmentation for dental X-ray images using texture-based fuzzy inference system." in Advanced Concepts for Intelligent Vision Systems: 10th International Conference, ACIVS 2008, Juan-les-Pins, France, October 20-24, 2008. Proceedings 10, (2008): Springer, pp. 936-47.
23- Abdolvahab Ehsani Rad, Mohd Shafry Mohd Rahim, and Alireza Norouzi, "Digital dental X-ray image segmentation and feature extraction." TELKOMNIKA Indonesian Journal of Electrical Engineering, Vol. 11 (No. 6), pp. 3109-14, (2013).
24- Jiangchang Xu, Shiming Wang, Zijie Zhou, Jiannan Liu, Xiaoyi Jiang, and Xiaojun Chen, "Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net." International Journal of Computer Assisted Radiology and Surgery, Vol. 15pp. 1457-65, (2020).
25- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, "ImageNet classification with deep convolutional neural networks." Communications of the ACM, Vol. 60 (No. 6), pp. 84-90, (2017).
26- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, "Deep residual learning for image recognition." in Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), pp. 770-78.
27- Christian Szegedy et al., "Going deeper with convolutions." in Proceedings of the IEEE conference on computer vision and pattern recognition, (2015), pp. 1-9.
28- K Vijayakumar, V Rajinikanth, and MK Kirubakaran, "Automatic detection of breast cancer in ultrasound images using Mayfly algorithm optimized handcrafted features." Journal of X-Ray Science and Technology, Vol. 30 (No. 4), pp. 751-66, (2022).
29- Ali B Syed and Adam C Zoga, "Artificial intelligence in radiology: current technology and future directions." in Seminars in musculoskeletal radiology, (2018), Vol. 22 (No. 05): Thieme medical publishers, pp. 540-45.
| Files | ||
| Issue | Vol 13 No 1 (2026) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v13i1.20786 | |
| Keywords | ||
| : Algorithm Caries Deeper Google Net mini VGG Net Res Net | ||
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How to Cite
1.
Divakaran S, K V. Dental X –Ray Images for Automated Detection of Caries Classes Using Deep Learning Techniques. Frontiers Biomed Technol. 2026;13(1):206-211.
