Enhanced Dental Diagnostics: Caries Detection and Sizing in Panoramic X-Ray Image | ||
| مجلة القراءة والمعرفة | ||
| Volume 25, Issue 288, October 2025 PDF (1.21 M) | ||
| Document Type: المقالة الأصلية | ||
| DOI: 10.21608/mrk.2025.462071 | ||
| Author | ||
| Shahad Saad Ibrahim Alhawiti* | ||
| Abstract | ||
| This study introduces a comprehensive framework that utilizes deep learning techniques for the automated detection and segmentation of dental caries in panoramic X-ray images. The proposed system is designed to assist dental practitioners by highlighting regions of decay and accurately outlining their boundaries, thereby facilitating more efficient and precise diagnoses. A two-stage model, Faster R-CNN, is employed for object detection due to its effectiveness in identifying carious lesions of varying sizes with enhanced localization accuracy. For the task of semantic segmentation, the DeepLabV3+ architecture is implemented to generate detailed pixel-level masks of the affected regions, providing a more granular understanding of the decay patterns. The overall approach encompasses image preprocessing, model development, and performance evaluation using established quantitative metrics. The experimental results indicate the capability and robustness of the proposed method in improving diagnostic accuracy. These findings underscore the potential of this framework to serve as a valuable clinical aid and pave the way for the future integration of AI-powered dental analysis tools into modern dental practice. | ||
| Keywords | ||
| Enhanced Dental Diagnostics; Caries Detection; Sizing in Panoramic; X; Ray Image | ||
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