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Computers and Computing

A Deep Learning Approach based on Faster R-CNN for Automatic Detection and Classification of Teeth in Orthopantomogram Radiography Images

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References

  • R. M. Baiju, E. Peter, N. O. Varghese, and R. Sivaram, “Oral health and quality of life: Current concepts,” J. Clin. Diagn. Res., Vol. 11, no. 6, pp. ZE21–6, 2017. DOI: 10.7860/JCDR/2017/25866.10110.
  • M. Yewe-Dyer, “The definition of oral health,” Br. Dent. J., Vol. 174, pp. 224–5, 1993.
  • C. C. Peck. Putting the Mouth into Health: The Importance of Oral Health for General Health, Interface Oral Health Science 2016. Singapore: Springer, 2017, pp. 81–7. DOI: 10.1007/978-981-10-1560-1_7.
  • V. Mehrotra, P. Devi, T. V. Bhovi, and B. Jyoti, “Mouth as a mirror of systemic diseases,” Gomal J. Med. Sci., Vol. 8, pp. 235–41, 2010.
  • D. Hallikainen, “History of panoramic radiography,” Acta Radiol., Vol. 37, no. 3, pp. 441–5, 1996. DOI: 10.3109/02841859609177678.
  • J. W. Choi, “Assessment of panoramic radiography as a national oral examination tool: Review of the literature,” Imaging Sci. Dent., Vol. 41, pp. 1–6, 2011. DOI: 10.5624/isd.2011.41.1.1.
  • A. Fourcade, and R. H. Khonsari, “Deep learning in medical image analysis: A third eye for doctors,” J. Stomatol. Oral Maxillofac. Surg., Vol. 120, no. 4, pp. 279–88, 2019.
  • A. Maier, C. Syben, T. Lasser, and C. Riess, “A gentle introduction to deep learning in medical image processing,” Z Med Phys, Vol. 29, pp. 86–101, 2019.
  • M. Bakator, and D. Radosav, “Deep learning and medical diagnosis: A review of literature,” Multimodal. Technol. Interact., Vol. 2, pp. 4–7, 2018.
  • M. Prados-Privado, J. G. Villalón, C. H. Martínez-Martínez, and C. Ivorra, “Dental images recognition technology and applications: A literature review,” Appl. Sci., Vol. 10, pp. 28–56, 2020.
  • A. F. Leite, K. F. Vasconcelos, H. Willems, and R. Jacobs, “Radiomics and machine learning in oral healthcare,” Proteom Clin. Appl., Vol. 14, no. 3, 2020. DOI: 10.1002/prca.201900040.
  • J. J. Hwang, Y. H. Jung, B. H. Cho, and M. S. Heo, “An overview of deep learning in the field of dentistry,” Imaging Sci Dent., Vol. 49, pp. 1–7, 2019.
  • K. Chauhan, and S. Ram, “Image classification with deep learning and comparison between different convolutional neural network structures using Tensorflow and Keras,” Int. J. Adv. Eng. Res. Dev., Vol. 5, 2018.
  • J. Yang, Y. Xie, L. Liu, B. Xia, Z. Cao, and C. Guo. “Automated dental image analysis by deep learning on small dataset,” IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), 2018 Tokyo, Vol. 1, pp. 492–7.
  • F. Schwendicke, T. Golla, M. Dreher, and J. Krois, “Convolutional neural networks for dental image diagnostics: A scoping review,” J. Dent., Vol. 91, 2019.
  • Y. Yu. “Machine learning for dental image analysis,” ArXiv, abs/1611.09958, 2016.
  • S. K. Jung, and T. W. Kim, “New approach for the diagnosis of extractions with neural network machine learning,” Am. J. Orthod Dentofacial Orthop., Vol. 149, pp. 127–33, 2016.
  • K. Yasaka, and O. Abe, “Deep learning and artificial intelligence in radiology: Current applications and future directions,” PLOS Med., Vol. 15, 2018.
  • M. P. McBee, et al., “Deep learning in radiology,” Acad. Radiol., Vol. 25, pp. 1472–80, 2018.
  • A. S. Lundervold, and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Z Med. Phys., Vol. 29, pp. 102–127, 2019.
  • J. Y. Chiao, K. Y. Chen, K. Y. Liao, P. H. Hsieh, G. Zhang, and T. C. Huang, “Detection and classification the breast tumors using mask R-CNN on sonograms,” Medicine (Baltimore), Vol. 98, 2019.
  • F. Casalegno, et al., “Caries detection with near-infrared transillumination using deep learning,” J Dent Res., Vol. 98, pp. 1227–33, 2019.
  • H. Chen, K. Zhang, P. Lyu, H. Li, L. Zhang, J. Wu, and C. H. Lee, “A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films,” Sci. Rep., Vol. 9, 2019.
  • R. B. Ali, R. Ejbali, and M. Zaied. “Detection and classification of dental caries in X-ray images using deep neural networks,” ICSEA 2016: The Eleventh International Conference on Software Engineering Advances, Rome, Italy, 2016, pp. 223–227.
  • W. Poedjiastoeti, and S. Suebnukarn, “Application of convolutional neural network in the diagnosis of Jaw tumors,” Healthc Inform Res., Vol. 24, pp. 236–24, 2018.
  • A. B. Oktay. “Tooth detection with convolutional neural networks,” 2017 Medical Technologies National Congress (TIPTEKNO), Trabzon, 2017, pp. 1–4.
  • Y. Miki, C. Muramatsu, T. Hayashi, X. Zhou, T. Hara, A. Katsumata, and H. Fujita, “Classification of teeth in cone-beam CT using deep convolutional neural network,” Comput. Biol. Med., Vol. 80, pp. 24–29, 2016.
  • N. H. Lin, et al., “Teeth detection algorithm and teeth condition classification based on convolutional neural networks for dental panoramic radiographs,” J. Med. Imag. Health In, Vol. 8, pp. 507–15, 2018.
  • A. Laishram, and K. Thongam. “Detection and classification of dental pathologies using faster-rcnn in orthopantomogram radiography image,” 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), 2020, pp. 423–428. DOI: 10.1109/SPIN48934.2020.9071242.
  • A. Laishram, and K. Thongam, “Automatic classification of oral pathologies using orthopantomogram radiography images based on convolutional neural network,” Int. J. Interact. Multimedia Artif. Intell., Vol. 7, pp. 69–77, 2022.
  • S. Indolia, A. Goswami, S. P. Mishra, and P. Asopa, “Conceptual understanding of convolutional neural network - A deep learning approach,” Procedia Comput. Sci., Vol. 132, pp. 679–88, 2019.
  • S. Ren, K. He, R. Girshick, and S. Jian, “Faster R-CNN: towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 39, pp. 1137–49, 2017.
  • U. Khan. “Prostate cancer detection using deep learning,” 2019. Available: https://urn.fi/URN:NBN:fi:tuni-201907082497.
  • Y. Mahesha, and C. Nagaraju. “Machine learning approach to detect congenital heart diseases using Angle at Axial Triradius,” 2021 IEEE Mysore Sub Section International Conference (MysuruCon), 2021, pp. 220–226.
  • J. Krois, et al., “Deep learning for the radiographic detection of periodontal bone loss,” Sci. Rep., Vol. 9, 2019.
  • P. Singh, and P. Sehgal, “Numbering and classification of panoramic dental images using 6-layer convolutional neural network,” Pattern Recognit. Image Anal, Vol. 30, pp. 125–33, 2020.
  • L. Kats, M. Vered, A. Z. Hurvitz, and I. Harpaz, “Atherosclerotic carotid plaque on panoramic radiographs: neural network detection,” Int. J. Comput. Dent., Vol. 22, pp. 163–9, 2019.

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