Segmentation of Brain Tumors Using a Generative AI-Enhanced U-Net Model

Generative AI is significantly transforming medical imaging by helping to overcome major obstacles such as data scarcity, the high cost of image annotation, and privacy concerns. This research explores the utilization of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) for tasks including image generation, denoising, scan reconstruction, and the segmentation of anatomical structures. Using the publicly available BRATS2020 dataset, we implemented models such as GANs, VAEs, and the U-Net architecture. Performance was evaluated using metrics including the Structural Similarity Index (SSIM), the Dice Coefficient, and the Fréchet Inception Distance (FID). The results demonstrated a significant improvement in image quality and segmentation accuracy. A primary application was the automation of brain tumor segmentation from MRI scans, a task that is traditionally time-consuming, labor-intensive, and subject to inter-observer variability. This research presents an automated method using a U-Net convolutional neural network, which is specifically designed for biomedical image segmentation. The process involved data preparation, implementation of the U-Net model, and a thorough training phase. The model's performance was assessed using the Dice Similarity Coefficient (DSC) and Intersection over Union (IoU). The U-Net model achieved promising results in accurately segmenting brain tumor regions, demonstrating high potential as an effective tool for automated tumor outlining. On the BRATS2020 test data, the model achieved an accuracy of 99.18%, indicating high efficiency and superior performance compared to current state-of-the-art research. This work contributes to the growing field of deep learning in medical imaging by providing a detailed framework and reproducible results for brain tumor segmentation.