Brain tumor classification using GAN-augmented data with autoencoders and Swin Transformers.

Introduction: Brain tumor classification remains one of the most challenging tasks in medical image analysis, with diagnostic errors potentially leading to severe consequences. Existing methods often fail to fully exploit all relevant features, focusing on a limited set of deep features that may miss the complexity of the task. Methods: In this paper, we propose a novel deep learning model combining a Swin Transformer and AE-cGAN augmentation to overcome challenges such as data imbalance and feature extraction. AE-cGAN generates synthetic images, enhancing dataset diversity and improving the model's generalization. The Swin Transformer excels at capturing both local and global dependencies, while AE-cGAN generates synthetic data that enables classification of multiple brain tumor morphologies. Results: The model achieved impressive accuracy rates of 99.54% and 98.9% on two publicly available datasets, Figshare and Kaggle, outperforming state-of-the-art methods. Our results demonstrate significant improvements in classification, sensitivity, and specificity. Discussion: These findings indicate that the proposed approach effectively addresses data imbalance and feature extraction limitations, leading to superior performance in brain tumor classification. Future work will focus on real-time clinical deployment and expanding the model's application to various medical imaging tasks.