Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care


  • Shivampeta Aparna Gitam University, Hyderabad Campus, India
  • Himabindu Gottumukkala GITAM School of Technology, India
  • Nitya Shivampet Oak Park High School, Thousand Oaks, CA, USA
  • Kireet Muppavaram GITAM School of Technology, India
  • Chaitanya C. V. Ramayanam University of Southern California, Los Angeles, CA, USA
Volume: 14 | Issue: 3 | Pages: 14470-14474 | June 2024 |


Many individuals face issues with their teeth, requiring the expertise of dentists to provide necessary care. Despite the advancements in dental techniques, there is a persistent shortage of dentists, prompting the development of tools to help the latter efficiently perform patient treatment. The current research focuses on refining the precision of the vital dental treatment known as dental fillings. The approach involves utilizing the Mask Region-based Convolutional Neural Network (MaskRCNN) with different variants of ResNET, such as ResNET50, ResNET101 C4, Dilated C5, and Feature Pyramid Network (FPN), to analyze diverse dental radiographs. By training on a broad range of tooth images, this methodology creates a pixel-based masking system, improving dentists' ability to precisely identify filling levels. Consequently, this innovation contributes significantly to expediting and refining the accuracy of dental treatments, ultimately benefiting individuals with tooth problems. Additionally, as a future prospect, this model can enable robots to perform dental operations as it provides pixel-level information necessary for the treatment.


dental x-rays, RESNET, MaskRCNN, annotations, dental fillings, FPN, ROI pooling


Download data is not yet available.


V. Sa-ing, K. Wangkaoom, and S. S. Thongvigitmanee, "Automatic dental arch detection and panoramic image synthesis from CT images," in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, Jul. 2013, pp. 6099–6102.

F. Shamsafar, "A new feature extraction method from dental X-ray images for human identification," in 2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP), Zanjan, Iran, Sep. 2013, pp. 397–402.

A. Ajaz and D. Kathirvelu, "Dental biometrics: Computer aided human identification system using the dental panoramic radiographs," in 2013 International Conference on Communication and Signal Processing, Melmaruvathur, India, Apr. 2013, pp. 717–721.

L. Čular, M. Tomaić, M. Subašić, T. Šarić, V. Sajković, and M. Vodanović, "Dental age estimation from panoramic X-ray images using statistical models," in Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, Ljubljana, Slovenia, Sep. 2017, pp. 25–30.

N. C. Kundur, B. C. Anil, P. M. Dhulavvagol, R. Ganiger, and B. Ramadoss, "Pneumonia Detection in Chest X-Rays using Transfer Learning and TPUs," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11878–11883, Oct. 2023.

N. Kumar, A. Hashmi, M. Gupta, and A. Kundu, "Automatic Diagnosis of Covid-19 Related Pneumonia from CXR and CT-Scan Images," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 7993–7997, Feb. 2022.

S. Aparna, K. Muppavaram, C. C. V. Ramayanam, and K. S. S. Ramani, "Mask RCNN with RESNET50 for Dental Filling Detection," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 12, no. 10, 54/31 2021.

S. Divakaran, K. Vasanth, S. D, and S. V, "Classification of Digital Dental X-ray Images Using Machine Learning," in 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, India, Mar. 2021, pp. 1–3.

H. Chen et al., "A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films," Scientific Reports, vol. 9, no. 1, Mar. 2019, Art. no. 3840.

M. Salemdeeb and S. Erturk, "Multi-national and Multi-language License Plate Detection using Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 5979–5985, Aug. 2020.

S. Mukherjee, "The Annotated ResNet-50," Towards Data Science, Aug. 18, 2022.

K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition." arXiv, Apr. 10, 2015.

R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," in 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, Jun. 2014, pp. 580–587.

F. Yu, V. Koltun, and T. Funkhouser, "Dilated Residual Networks." arXiv, May 28, 2017.

S.-H. Tsang, "Review: DRN — Dilated Residual Networks (Image Classification & Semantic Segmentation)," Towards Data Science, Mar. 20, 2019.

S.-H. Tsang, "Review: FPN — Feature Pyramid Network (Object Detection)," Towards Data Science, Mar. 20, 2019.


How to Cite

S. Aparna, H. Gottumukkala, N. Shivampet, K. Muppavaram, and C. C. V. Ramayanam, “Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14470–14474, Jun. 2024.


Abstract Views: 163
PDF Downloads: 51

Metrics Information