Acta Scientific Computer Sciences

Editorial Volume 7 Issue 7

Hybrid Deep Learning for Detecting Gum Disease – A Simple Guide

Caroline Gnanatheepa KT*

Assistant Professor, Department of Computer Science, S.I.V.E.T. College, Chennai, India

*Corresponding Author: Caroline Gnanatheepa KT, Assistant Professor, Department of Computer Science, S.I.V.E.T. College, Chennai, India.

Received: July 21, 2025; Published: September 01, 2025

Abstract

Periodontal disease—a major cause of tooth loss—affects nearly half the global adult population. Detecting it early is crucial. Typically, dentists use X-rays (panoramic or periapical) to check for bone loss, but this process is slow and can vary depending on the dentist reading the image.

References

  1. HJ Chang., et al. “Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis”. Scientific Reports  7531 (2020).
  2. J Zhao., et al. “Hybrid Deep Learning Ensemble for Medical Image Segmentation: Combining YOLOv8, Mask R-CNN, and TransUNet”. IEEE Access 12 (2024): 45678-45690.
  3. T Kabir., et al. “An end-to-end entangled segmentation and classification convolutional neural network for periodontitis stage grading from periapical radiographic images”. arXiv (2021).
  4. J Jundaeng., et al. “Artificial intelligence-powered innovations in periodontal diagnosis: a new era in dental healthcare”. Frontiers in Medical Technology 1469852 (2025).
  5. J Chen., et al. “TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation”. arXiv preprint arXiv:2102.04306 (2021).
  6. R Chartsias., et al. “Disentangled representation learning in cardiac image analysis”. Medical Image Analysis 58 (2019): 101535.

Citation

Citation: Caroline Gnanatheepa KT. “Hybrid Deep Learning for Detecting Gum Disease – A Simple Guide".Acta Scientific Computer Sciences 7.7 (2025): 01-02.

Copyright

Copyright: © 2025 Caroline Gnanatheepa KT. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate35%
Acceptance to publication20-30 days

Indexed In




News and Events


Contact US