Detection and Classification of Periapical Dental X-Ray Images Using Machine Learning

Dental X-ray segmentation process employs a variety of image processing algorithms that are useful in medical diagnostics, clinical functions, and time period application. The division of discrete tooth characteristics that are discernible in dental X-ray pictures and that are useful, among other things, for the early detection of periodontal diseases, decay, and fractured teeth. This is crucial for illness diagnosis. In clinical practice, dental X-ray image manual segmentation and classification for diagnosis from large databases are an extremely complex and time-consuming process. The X-ray images are first pre-processed to remove unwanted noises and other effects before a threshold method using morphological operations is used to separate the impacted tooth components from the radiograph in order to classify and segment the affected teeth. The image is segmented using the K-means clustering method after pre-processing in order to obtain the region of interest and to produce better results followed by texture features are extracted using a gray-level co-occurrence matrix. The various types of cavities are classified using the Multiclass SVM Classifier. The proposed method outcomes show a high level of efficiency and accuracy in the teeth measurement process.