Abstract
Dental caries is a prevalent noncommunicable disease that affects over half of the global population. It can significantly diminish individuals’ quality of life by impairing their eating and socializing abilities. Consistent dental check-ups and professional oral healthcare are crucial in preventing dental caries and other oral diseases. Deep learning based object detection provides an efficient approach to assist dentists in identifying and treating dental caries. In this paper, we present a deep learning framework with a lightweight pruning region of interest (P-RoI) proposal specifically designed for detecting dental caries in panoramic dental radiographic images. Moreover, this framework can be enhanced with an auxiliary head for label assignment during the training process. By utilizing the Cascade Mask R-CNN model with a ResNet-101 backbone as the baseline, our modified framework with the P-RoI proposal and auxiliary head achieves a notable 3.85 increase in Average Precision (AP) for the dental caries class within our dental dataset.
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References
Global oral health status report. World Health Organization
Cai, Z., Vasconcelos, N.: Cascade R-CNN: high quality object detection and instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 43(5), 1483–1498 (2019)
Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Haghanifar, A., Majdabadi, M.M., Ko, S.B.: Paxnet: dental caries detection in panoramic X-ray using ensemble transfer learning and capsule classifier. arXiv preprint arXiv:2012.13666 (2020)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Howard, A., et al.: Searching for mobilenetv3. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1314–1324 (2019)
Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Imak, A., Celebi, A., Siddique, K., Turkoglu, M., Sengur, A., Salam, I.: Dental caries detection using score-based multi-input deep convolutional neural network. IEEE Access 10, 18320–18329 (2022)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Ma, N., Zhang, X., Zheng, H.T., Sun, J.: Shufflenet V2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 116–131 (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Saini, D., Jain, R., Thakur, A.: Dental caries early detection using convolutional neural network for tele dentistry. In: 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), vol. 1, pp. 958–963. IEEE (2021)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Singh, N.K., Faisal, M., Hasan, S., Goshwami, G., Raza, K.: Dental treatment type detection in panoramic X-rays using deep learning. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds.) ISDA 2022. LNNS, vol. 716, pp. 25–33. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35501-1_3
Singh, N.K., Raza, K.: TeethU\(^2\)Net: a deep learning-based approach for tooth saliency detection in dental panoramic radiographs. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds.) ICONIP 2022. CCIS, vol. 1794, pp. 224–234. Springer, Cham (2023). https://doi.org/10.1007/978-981-99-1648-1_19
Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464–7475 (2023)
Welikala, R.A., et al.: Automated detection and classification of oral lesions using deep learning for early detection of oral cancer. IEEE Access 8, 132677–132693 (2020)
Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)
Zhu, B., et al.: Autoassign: differentiable label assignment for dense object detection. arXiv preprint arXiv:2007.03496 (2020)
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Wang, X. et al. (2024). A Deep Learning Framework with Pruning RoI Proposal for Dental Caries Detection in Panoramic X-ray Images. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14449. Springer, Singapore. https://doi.org/10.1007/978-981-99-8067-3_39
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DOI: https://doi.org/10.1007/978-981-99-8067-3_39
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