Deep Learning-Based Teeth Disease Detection Using Dental X-ray Images

Abstract

Oral health is vital for overall well-being. However, dental problems like cavities, gum disease, and infections are still very common around the world. Early diagnosis is crucial to avoid serious issues. Traditional detection methods depend largely on the expertise of clinicians, visual checks, and manual reading of dental X-rays. These methods can be subjective and time-consuming, leading to inconsistent results. Recent advances in artificial intelligence have introduced machine learning techniques as useful tools for improving medical image analysis and providing automated support for healthcare professionals. This study explores how machine learning can be used to detect dental diseases through dental X-rays. The research included systematic preprocessing of image data, with steps like normalizing images and enhancing features. Researchers then applied feature extraction methods to find important dental structures and disease indicators. Various algorithms, including decision trees, support vector machines, and convolutional neural networks, were trained and tested to classify healthy teeth against diseased ones. Among these, CNN-based deep learning models showed better results because they can automatically learn important features from the image data. The most acceptabble accuracy via MobileNetV2 ia 85.88%. The experimental results showed high accuracy, precision, and recall. This confirms that machine learning models can help with reliable and efficient dental disease detection. Additionally, the findings suggest that these systems can reduce the diagnostic workload, improve early intervention, and better patient outcomes. This research adds to the evidence that supports the use of artificial intelligence in dentistry. It provides a framework for incorporating machine learning into dental diagnostics. Ultimately, this approach highlights how technology can transform modern healthcare, making oral care more accurate, accessible, and preventive.