Semantic Segmentation on Panoramic Dental X-Ray Images Using U-Net Architectures

The field of medical image analysis is in a constant state of evolution, particularly in the challenging tasks of segmenting organs, diseases, and abnormalities. Therefore, in the realm of dental disease diagnosis, image segmentation plays a crucial role in addressing the difficulties faced by dentists worldwide when diagnosing dental diseases with the naked eye. One prominent deep neural network architecture, known as U-Net, originally designed for biomedical image segmentation, has seen multiple variations and advancements aimed at improving its performance. However, the lack of comparative studies has made it challenging to assess the effectiveness of these U-Net variants in segmenting dental X-ray images. The primary objective of this research is to conduct a comprehensive performance comparison among various U-Net architectures…