Brain Tumor Classification Using GAN-Augmented Data with Autoencoders and Swin Transformers

Abdullah Almuhaimeed¹Anas Bilal²Abdulkareem Alzahrani³Malek Alrashidi⁴Mansoor Al- Ghamdi⁴Raheem Sarwar^5*

• ¹King Abdulaziz City for Science And Technology, Riyadh, Saudi Arabia
• ²Hainan Normal University, Haikou, China
• ³Albaha University, Al Aqiq, Saudi Arabia
• ⁴University of Tabuk, Tabuk, Saudi Arabia
• ⁵Manchester Metropolitan University, Manchester, United Kingdom

Brain tumor classification remains one of the most challenging tasks in medical image analysis, with diag-nostic 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. 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 im-ages, enhancing dataset diversity and improving the model's generalization. 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. 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. Our results demonstrate significant improvements in classification, sensitivity, and specificity. Future work will focus on real-time clinical deployment and expanding the model’s application to various medical im-aging tasks.