Predicting Glioma for Prognosis using Generative Adversarial Network

This work introduces a state-of-the-art machine learning pipeline for glioma diagnosis, with the aim of improving diagnostic precision and reliability. The pipeline starts with MRI images, normalized to a standard scale using Z-score normalization to guarantee uniform data scaling. An adaptive Wiener filter subsequently denoises the images, enhancing image quality for subsequent processing. To counter the limitation of limited medical image availability, Generative Adversarial Networks (GANs) synthesize high-quality MRI data, increasing the training set. U-Net is used for feature extraction while maintaining important spatial information necessary for effective tumor detection. Lastly, a VGG-16 model classifies gliomas using deep convolutional layers to identify complex patterns that suggest tumor presence. This combined system greatly enhances glioma detection and offers clinicians an effective tool for accurate diagnosis and highlights the revolutionizing potential of machine learning in medical imaging.