Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

The brain tumor is an abnormal and hysterical growth of brain tissues, and the leading cause of death affected patients worldwide. Even in this technol-ogy-based arena, brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones. The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data. To over-come the highlighted issue, a Generative Adversarial Network (GAN) deep learn-ing technique in which two neural networks compete to become more accurate in creating artificially realistic data for MRI images. The GAN network contains mainly two parts known as generator and discriminator. Commonly, a generator is the convolutional neural network, and a discriminator is the deconvolutional neural network. In this research, the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging (CE-MRI) dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been used. Our proposed method is simple and achieved an accuracy of 96%. We compare our technique results with the existing results, indicating that our proposed technique outper-forms the best results associated with the existing methods.