Abstract
This work primarily focuses on convolutional neural networks (CNNs) and quantitatively analyzes dental images using deep learning models. Tooth separation is considerably improved when X-rays and computer images are used for dental procedure planning, diagnosis, and treatment. The aim of the research is to examine the performance of cutting-edge segmentation models on publicly available dental image datasets. The study demonstrates that CNN-based techniques consistently outperformed conventional machine learning models in terms of accuracy and robustness, especially when compared to noisy and low contrast images. According to these findings, it is possible to create efficient computer-aided detection (CAD) tools that will help dentists diagnose patients. By using Explainable AI, we can improve confidence and simplify the usage of autonomous diagnostic systems in dentistry.
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