Generative adversarial network with resnet discriminator for brain tumor classification

Deep learning has been used to classify brain tumor techniques and has become a vital field in medical research and diagnosis. In this study, we present an innovative method for brain tumor classification by applying generative adversarial networks (GANs). Specifically, we developed a GAN model with a modified ResNet architecture for the generator and a DenseNet architecture for the discriminator. This novel architecture harnesses the power of generative and discriminative networks to improve the accuracy and efficiency of brain tumor classification, providing a major breakthrough in the medical imaging field. The generator is based on a modified ResNet and is designed to produce realistic, high-resolution brain tumor images. We learn how to create synthetic brain scans that mimic the characteristics of real tumor images, helping to augment and diversify our data. This augmentation process is critical to effectively train deep learning models, especially when medical image availability is limited. Meanwhile, the discriminator uses a DenseNet architecture to distinguish between real brain tumor images and synthetic images produced by a ResNet-based generator. DenseNet's ability to capture the intricate details and features of medical images allows the discriminator to effectively distinguish between real and synthetic data, contributing to the overall learning process of the GAN. Our proposed GAN model is trained on a diverse and curated dataset of brain tumor images and is able to identify and classify different tumor types and their features with remarkable accuracy. The generated synthetic images help improve the model's ability to generalize and adapt to new, unknown data, thereby improving its performance in classifying brain tumors.