Open Access Open Access  Restricted Access Subscription Access

A Comparative Study on Panoramic X- Rays Disease Detection and Classification using Deep Learning Techniques

Ajas Muhammed N, Adarsh V, Yethu Krishnan, Adithyan K P, Sabeena K, Haseena P Y

Abstract


Panoramic X-rays play a vital role in dental practice by providing a comprehensive view of the oral cavity and aiding in treatment planning. However, interpreting these X-rays can be time-consuming and may result in misdiagnoses, especially for general practitioners who may not have specialized radiology training. The advancement of artificial intelligence (AI) holds promise in automating dental radiology analysis.This study exam- ines the application of deep learning, specifically Convolutional Neural Networks (CNNs), in detecting dental diseases. We explore different techniques based on Deep Learning for disease detection and classification in dentistry, emphasizing their potential to improve diagnostic accuracy and enhance dental healthcare.


Full Text:

PDF

References


. H. Organization, “www. who. int/news-room/fact-sheets/detail/oral- health,”

S.-L. Chen, T.-Y. Chen, Y.-C. Mao, S.-Y. Lin, Y.-Y. Huang, C.-A. Chen, Y.-J. Lin, Y.-M. Hsu, C.-A. Li, W.-Y. Chiang, et al., “Automated detection system based on convolution neural networks for retained root, endodontic treated teeth, and implant recognition on dental panoramic images,” IEEE Sensors Journal, vol. 22, no. 23, pp. 23293–23306, 2022.

A. S. AL-Ghamdi, M. Ragab, S. A. AlGhamdi, A. H. Asseri, R. F. Mansour, D. Koundal, et al., “Detection of dental diseases through x- ray images using neural search architecture network,” Computational Intelligence and Neuroscience, vol. 2022, 2022.

Y. E. Almalki, A. I. Din, M. Ramzan, M. Irfan, K. M. Aamir, A. Almalki,

S. Alotaibi, G. Alaglan, H. A. Alshamrani, and S. Rahman, “Deep learning models for classification of dental diseases using orthopanto- mography x-ray opg images,” Sensors, vol. 22, no. 19, p. 7370, 2022.

T. Lakshmi and J. Dheeba, “Classification and segmentation of peri- odontal cyst for digital dental diagnosis using deep learning,” Computer Assisted Methods in Engineering and Science, vol. 30, no. 2, pp. 131– 149, 2022.

D. Vasdev, V. Gupta, S. Shubham, A. Chaudhary, N. Jain, M. Salimi, and A. Ahmadian, “Periapical dental x-ray image classification using deep neural networks,” Annals of Operations Research, 2022.

M. A. Hasnain, S. Ali, H. Malik, M. Irfan, and M. S. Maqbool, “Deep learning-based classification of dental disease using x-rays,” Journal of Computing & Biomedical Informatics, vol. 5, no. 01, pp. 82–95, 2023.

M. G. Endres, F. Hillen, M. Salloumis, A. R. Sedaghat, S. M. Niehues,

O. Quatela, H. Hanken, R. Smeets, B. Beck-Broichsitter, C. Rendenbach, et al., “Development of a deep learning algorithm for periapical disease detection in dental radiographs,” Diagnostics, vol. 10, no. 6, p. 430, 2020.

Y.-C. Mao, Y.-C. Huang, T.-Y. Chen, K.-C. Li, Y.-J. Lin, Y.-L. Liu, H.-

R. Yan, Y.-J. Yang, C.-A. Chen, S.-L. Chen, et al., “Deep learning for dental diagnosis: A novel approach to furcation involvement detection on periapical radiographs,” Bioengineering, vol. 10, no. 7, p. 802, 2023.

M. P. Muresan, A. R. Barbura, and S. Nedevschi, “Teeth detection and dental problem classification in panoramic x-ray images using deep learning and image processing techniques,” in 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 457–463, IEEE, 2020.


Refbacks

  • There are currently no refbacks.