Brain Tumor Classification Using a Hybrid Learning Strategy Integrating GAN-Augmented Neural Networks

Abstract

Brain tumor (BT) classification plays a vital role in computer-aided diagnosis, as early detection directly influences treatment planning and patient survival. Advances in artificial intelligence, particularly in machine learning (ML) and deep learning (DL), have greatly improved automated tumor identification from magnetic resonance imaging (MRI), reducing reliance on manual assessment. This study proposes a hybrid framework that combines generative adversarial networks (GANs), convolutional neural networks (CNNs), and Vision Transformers (ViTs) for accurate tumor classification. GANs are used to generate realistic synthetic MRI images, addressing data scarcity and increasing dataset diversity. CNNs extract discriminative deep features, accelerating training while limiting overfitting. These features are then processed by a ViT model, capable of capturing complex spatial relationships within medical images. Experimental results show that the proposed ViT–CNN–GAN approach achieves 97.36% accuracy, outperforming conventional methods. Overall, this framework demonstrates the potential of advanced DL models to strengthen MRI-based tumor diagnosis and support more reliable clinical decision-making.