Abstract
Dental X-ray imaging helps dentists detect many problems such as caries, cysts and jaw structure problems. Clinical diagnosis and preventive examinations of dental structures play an important role by providing a comprehensive imaging evaluation with panoramic x-rays for dentists. However, researchers primarily use image processing methods to analyze and improve a dental X-ray image and increase its contribution to the diagnostic time. Image segmentation, classification, threshold-based analysis, artificial neural networks, frequency-based methods are the most widely used image processing techniques to analyze medical images and assist in the development of computer aided medical diagnosis systems. In this study, images were analyzed in terms of noise removal by using convolutional neural networks and binary and wavelet filters to improve the images that were distorted and lost their clarity as a result of noise caused by various reasons during shooting. The performances of these methods were compared and it was seen that successful results were obtained in different noise types by using convolutional neural networks.
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