Xception-Based Brain Tumor Classification with GAN Augmentation and Multi-Technique Explainable AI

Abstract

Automated brain tumor classification is essential for early detection and treatment planning. This study addresses the critical gap in Bangladeshi population-specific diagnostic systems by developing a comprehensive framework that integrates Generative Adversarial Network (GAN)-based augmentation with deep transfer learning and explainable artificial intelligence for MRI-based brain tumor classification. The proposed approach utilizes the PMRAM dataset comprising 3,505 T1-weighted MRI images from Bangladeshi patients across four classes (glioma, meningioma, pituitary tumor, and normal). A custom deep convolutional GAN generates 300 synthetic images per class, resulting in 40% dataset augmentation. The Xception architecture with ImageNet pre-training is employed using a two-stage training strategy consisting of feature extraction followed by fine-tuning of the final 100 layers. Stratified 5-fold cross-validation is conducted to compare performance against DenseNet169, MobileNetV2, and InceptionV3, while multi-technique explainable AI (GradCAM++, Score-CAM, SHAP, and LIME) provides clinical interpretability. Quantitative XAI evaluation via attribution map IoU and deletion faithfulness metrics confirms that highlighted regions are decision-critical and spatially concordant. Experimental results show that the proposed Xception-based model achieves a mean accuracy of 98.08% ± 0.42%, outperforming DenseNet169 (97.66%), MobileNetV2 (97.40%), and InceptionV3 (96.98%). The model attains a mean AUC of 0.997 with an Expected Calibration Error of 0.0135, and statistical validation confirms significance (p = 0.036). Ablation studies further demonstrate the contributions of GAN augmentation (+3.33%), fine tuning (+6.88%), and transfer learning (+21.81%). The proposed framework provides a comprehensive benchmark for Bangladeshi brain tumor classification with accuracy approaching clinical grade performance and interpretable predictions, highlighting the effectiveness of GAN-based transfer learning for population specific medical AI systems.