DentaGAN - Smart Dental Image Segmentation

This project introduces a novel method for smart dental image segmentation by leveraging Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and the U-net architecture. The process begins with thorough data preprocessing, employing techniques such as noise reduction and image enhancement to ensure dataset quality and consistency. Following this, individual CNN, GAN, and U-net models are developed and trained. CNNs extract features from dental images, GANs generate realistic images for training, and U - net performs the segmentation task. Detailed descriptions of hardware and software configurations in the experimental setup ensure reproducibility, while key training parameters provide insights into the training process. The results showcase the model's superior performance through quantitative metrics like accuracy, precision, and recall, along with qualitative assessments comparing the segmentation results to ground truth annotations. In summary, this project presents a comprehensive approach to dental image segmentation, combining advanced neural network architectures with meticulous data preprocessing and thorough experimental setup. The integration of CNNs, GANs, and U-net models leads to enhanced segmentation accuracy, validated through both quantitative evaluations and qualitative comparisons with ground truth annotations.