Tooth cavities detection based on digital image processing and artificial intelligence techniques

Tooth cavities are primarily driven by sugar-induced bacterial activity that progressively erodes dental structures. Advances in medical image processing provide dentists with valuable tools to support accurate diagnosis and selection of appropriate therapeutic interventions, thereby improving oral healthcare. This study presents the development of an automated dental disease detection system, designed to reduce clinician workload, minimise diagnostic time, and lower the risk of human error. Dental radiographs are first subjected to noise reduction, greyscale conversion, filtering, and resizing, followed by the extraction of discriminative features. Key feature extraction methods include Wavelet analysis, Gray-Level Co-Occurrence Matrix (GLCM), and texture analysis. These features were subsequently used to train and evaluate machine learning classifiers, specifically Support Vector Machine (SVM) and Neural Network (NN) models. The system achieved classification accuracies of 80% with SVM and 77% with NN when all features were combined. The primary objective of the system is to classify dental X-ray images as normal or abnormal, and to further identify abnormalities such as caries. Compared to conventional diagnostic methods, the proposed automated approach enables faster and more reliable detection of dental diseases. Ultimately, this system has the potential to support dentists in clinical decision-making and enhance the quality of patient care.