Xception-Based Brain Tumor Classification with GAN Augmentation and Multi-Technique Explainable AI
TL;DR
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.
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.