Employing CNN ensemble models in classifying dental caries using oral photographs

Abstract: Dental caries is arguably the most persistent dental condition that affects most people over their lives. Carious lesions are commonly diagnosed by dentists using clinical and visual examination along with oral radiographs. In many circumstances, dental caries is challenging to detect with photography and might be mistaken as shadows for various reasons, including poor photo quality. However, with the introduction of Artificial Intelligence and robotic systems in dentistry, photographs can be a helpful tool in oral epidemiological research for the assessment of dental caries prevalence among the population. It can be used particularly to create a new automated approach to calculate DMF (Decay, Missing, Filled) index score. In this paper, an autonomous diagnostic approach for detecting dental cavities in photos is developed using deep learning algorithms and ensemble methods. The proposed technique employs a set of pretrained models including Xception, VGG16, VGG19, and DenseNet121 to extract essential characteristics from photographs and to classify images as either normal or caries. Then, two ensemble learning methods, E- majority and E-sum, are employed based on majority voting and sum rule to boost the performances of the individual pretrained model. Experiments are conducted on 50 images with data augmentation for normal and caries images, the employed E-majority and E-sum achieved an accuracy score of 96% and 97%, respectively. The obtained results demonstrate the superiority of the proposed ensemble framework in the detection of caries. Furthermore, this framework is a step toward constructing a fully automated, efficient decision support system to be used in the dentistry area.

How to cite this paper

 AlSayyed, A., Taqateq, A., Al-Sayyed, R., Suleiman, D., Shukri, S., Alhenawi, E & Albsheish, A. (2023). Employing CNN ensemble models in classifying dental caries using oral photographs.International Journal of Data and Network Science, 7(4), 1535-1550.

Refrences

Boumaraf, S., Liu, X., Zheng, Z., Ma, X., & Ferkous, C. (2021). A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images. Biomedical Signal Processing and Control, 63, 102192.
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE confer-ence on computer vision and pattern recognition (pp. 1251-1258).
Choudhary, A., Raj, G., Agrawal, A. P., Sawhney, H., Nand, P., & Bhargava, D. (2021, December). An effective approach for classification of dental caries using Convolutional Neural Networks. In 2021 10th International Conference on Sys-tem Modeling & Advancement in Research Trends (SMART) (pp. 204-209). IEEE.
Cunha, S. R., Maravic, T., Comba, A., Ramos, P. A., Tay, F. R., Pashley, D. H., ... & Breschi, L. (2020). In vivo and in vitro radiotherapy increased dentin enzymatic activity. Journal of Dentistry, 100, 103429.
Haghanifar, A., Majdabadi, M. M., & Ko, S. B. (2020). Paxnet: Dental caries detection in panoramic x-ray using ensemble transfer learning and capsule classifier. arXiv preprint arXiv:2012.13666.
Haghanifar, A. (2022). Automated Teeth Extraction and Dental Caries Detection in Panoramic X-ray (Doctoral disserta-tion, University of Saskatchewan).
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Pro-ceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
Imak, A., Celebi, A., Siddique, K., Turkoglu, M., Sengur, A., & Salam, I. (2022). Dental caries detection using score-based multi-input deep convolutional neural network. IEEE Access, 10, 18320-18329.
Johansson, U., Niklasson, L., & König, R. (2004, June). Accuracy vs. comprehensibility in data mining models. In Pro-ceedings of the seventh international conference on information fusion (Vol. 1, pp. 295-300). Stockholm, Sweden: Elsevier.
Kaur, T., & Gandhi, T. K. (2020). Deep convolutional neural networks with transfer learning for automated brain image classification. Machine vision and applications, 31(3), 20.
Lian, L., Zhu, T., Zhu, F., & Zhu, H. (2021). Deep learning for caries detection and classification. Diagnostics, 11(9), 1672.
Manna, A., Kundu, R., Kaplun, D., Sinitca, A., & Sarkar, R. (2021). A fuzzy rank-based ensemble of CNN models for clas-sification of cervical cytology. Scientific Reports, 11(1), 14538.
Noon, S. K., Amjad, M., Qureshi, M. A., & Mannan, A. (2020, November). Overfitting mitigation analysis in deep learn-ing models for plant leaf disease recognition. In 2020 IEEE 23rd International Multitopic Conference (INMIC) (pp. 1-5). IEEE.
Popa, L. (2021). A statistical framework for evaluating convolutional neural networks. Application to colon cancer. An-nals of the University of Craiova-Mathematics and Computer Science Series, 48(1), 159-166.
Revilla-León, M., Young, K., Sicilia, E., Cho, S. H., & Kois, J. C. (2022). Influence of definitive and interim restorative materials and surface finishing on the scanning accuracy of an intraoral scanner. Journal of dentistry, 120, 104114.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bot-tlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510-4520).

Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv pre-print arXiv:1409.1556.
Sinha, A., Ramish, M., Kumari, S., Jha, P., & Tiwari, M. K. (2022, January). ANN-ANT-LION-MLP Ensemble Transfer Learning Based Classifier for Detection and Classification of Oral Disease Severity. In 2022 12th International Con-ference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 530-535). IEEE.
Sonavane, A., Yadav, R., & Khamparia, A. (2021). Dental cavity classification of using convolutional neural network. In IOP conference series: materials science and engineering (Vol. 1022, No. 1, p. 012116). IOP Publishing.
Stark, B., & Samarah, M. (2019, December). Ensemble and Deep Learning for Real-time Sensors Evaluation of algorithms for real-time sensors with application for detecting brushing location. In 2019 IEEE 5th International Conference on Computer and Communications (ICCC) (pp. 555-559). IEEE.
Stoean, C., Stoean, R., Sandita, A., Ciobanu, D., Mesina, C., & Gruia, C. L. (2016). Svm-based cancer grading from histo-pathological images using morphological and topological features of glands and nuclei. In Intelligent interactive mul-timedia systems and services 2016 (pp. 145-155). Springer International Publishing.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).
Xue, D., Zhou, X., Li, C., Yao, Y., Rahaman, M. M., Zhang, J., ... & Sun, H. (2020). An application of transfer learning and ensemble learning techniques for cervical histopathology image classification. IEEE Access, 8, 104603-104618.
Zhu, H., Cao, Z., Lian, L., Ye, G., Gao, H., & Wu, J. (2022). CariesNet: a deep learning approach for segmentation of mul-ti-stage caries lesion from oral panoramic X-ray image. Neural Computing and Applications, 1-9.