Class-conditioned synthetic MRI generation using ACGAN to improve brain-tumor classification accuracy

Abstract Accurate classification of brain tumors is often limited by the scarcity of labeled medical images, particularly for complex tumor subtypes. This study proposes an auxiliary classifier generative adversarial network (ACGAN) to generate class-conditioned synthetic MRI images and to enhance the performance of tumor-classification models. The ACGAN was trained using the publicly available Figshare Brain MRI dataset, which contains four categories: glioma, meningioma, pituitary tumor, and healthy tissue. After training, the model produced high-fidelity synthetic MRI images that were quantitatively evaluated using the Fréchet inception distance (FID) as a distribution-level proxy metric, achieving a score of 4.6 and indicating close alignment between the real and synthetic data distributions. In addition to generative quality, the classification network trained with ACGAN-augmented data achieved 94.8% accuracy, 95.3% sensitivity, 93.6% specificity, and 94.1% F1-score, outperforming models trained solely on real data. Comparative analysis demonstrates that the proposed framework provides superior class separability and lower FID values than commonly used generative methods, including DCGAN and standard cGAN approaches. These findings highlight the potential of ACGAN-based synthetic augmentation to support medical-imaging tasks under limited-data conditions and to improve the reliability of brain-tumor classification systems.