Deep Transfer Learning Based Multi-Classification of Dental Diseases X-Ray Images

This research work proposes a deep transfer learning model for multi-classification purposes within dental diseases classed by their X-ray images. The paper elaborates typical challenges posed by dentistry, which faces many diagnosis issues; the decisive process of proper classification into types of diseases requires being fine and efficient. Most state-of-the-art methodologies developed through CNNs and DNNs have been found to yield fair results, but they both suffer from overfitting and are time-consuming processes. The new model uses the ResNet50 to get over those limitations. Dental X-ray pictorials are to be grouped into four distinct categories using the proposed architecture: cavities, fillings, impacted teeth, and implants. The system proposed should classify these four categories in X-ray images with an accuracy greater than 92%. Therefore, the proposed system will automatically identify areas requiring clinical treatment. Expected outcome is a strong and dependable tool for enhanced dental disease diagnosis by better accuracy, efficiency, and performance in the evvaluation of X-ray pictures.