Hybrid ConvViT model for classification of dental radiography images; [Dental radyografi görüntülerinin sınıflandırılmasına yönelik hibrit ConvViT modeli]

Dental radiography is used as an important diagnostic tool in the field of dentistry. Dental radiography provides detailed visualization of teeth, jaws and surrounding structures. Dental radiographs are manually interpreted by dentists for diagnosis and treatment purposes. Artificial intelligence methods are successfully applied to automate various image-related processes such as diagnosis and classification. In this study, a hybrid ConvViT model was developed to detect caries, fillings, impacted teeth, implants and normal teeth from dental radiography images. ConvViT combined the advantages of CNN and ViT architectures and provided more successful classification of dental radiography images. ConvViT was compared with popular pre-trained deep learning models such as ResNet-50, VGG-16, EfficientNetB0, and DenseNet201. Experimental results showed that ConvViT outperformed the compared models with 95% accuracy, 95% precision, 94% recall and 94% F-score. ConvViT makes a significant contribution to the development of artificial intelligence-based automatic diagnosis systems in the field of dentistry by effectively modeling both local and long-range features thanks to its hybrid structure.