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.
