Towards dental diagnostic systems: Synergizing wavelet transform with generative adversarial networks for enhanced image data fusion.

Autor: Al-Haddad AA; College of Dentistry, University of Baghdad, Baghdad, Iraq., Al-Haddad LA; Training and Workshops Center, University of Technology- Iraq, Baghdad, Iraq. Electronic address: Luttfi.a.alhaddad@uotechnology.edu.iq., Al-Haddad SA; Civil Engineering Department, University of Technology- Iraq, Baghdad, Iraq., Jaber AA; Mechanical Engineering Department, University of Technology- Iraq, Baghdad, Iraq., Khan ZH; Interdisciplinary Research Center for Intelligent Manufacturing & Robotics (IRC-IMR), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia., Rehman HZU; Department of Mechatronics and Biomedical Engineering, Air University (AU), Islamabad, Pakistan.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Nov; Vol. 182, pp. 109241. Date of Electronic Publication: 2024 Oct 02.
DOI: 10.1016/j.compbiomed.2024.109241
Abstrakt: The advent of precision diagnostics in pediatric dentistry is shifting towards ensuring early detection of dental diseases, a critical factor in safeguarding the oral health of the younger population. In this study, an innovative approach is introduced, wherein Discrete Wavelet Transform (DWT) and Generative Adversarial Networks (GANs) are synergized within an Image Data Fusion (IDF) framework to enhance the accuracy of dental disease diagnosis through dental diagnostic systems. Dental panoramic radiographs from pediatric patients were utilized to demonstrate how the integration of DWT and GANs can significantly improve the informativeness of dental images. In the IDF process, the original images, GAN-augmented images, and wavelet-transformed images are combined to create a comprehensive dataset. DWT was employed for the decomposition of images into frequency components to enhance the visibility of subtle pathological features. Simultaneously, GANs were used to augment the dataset with high-quality, synthetic radiographic images indistinguishable from real ones, to provide robust data training. These integrated images are then fed into an Artificial Neural Network (ANN) for the classification of dental diseases. The utilization of the ANN in this context demonstrates the system's robustness and culminates in achieving an unprecedented accuracy rate of 0.897, 0.905 precision, recall of 0.897, and specificity of 0.968. Additionally, this study explores the feasibility of embedding the diagnostic system into dental X-ray scanners by leveraging lightweight models and cloud-based solutions to minimize resource constraints. Such integration is posited to revolutionize dental care by providing real-time, accurate disease detection capabilities, which significantly reduces diagnostical delays and enhances treatment outcomes.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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