Xception-Based Brain Tumor Classification with GAN Augmentation and Multi-Technique Explainable AI
DOI:
https://doi.org/10.53799/cztp8n16Keywords:
Brain tumor classification, GAN augmentation, Transfer learning, Explainable AI, Xception, Deep learningAbstract
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.
References
[1] W. Wren et al., “Global incidence of brain and spinal tumors: regional variations and temporal trends,” Neuro Oncol., vol. 25, no. 3, pp. 456 468, 2023.
[2] S. Islam et al., “Healthcare challenges in Bangladesh: The need for AI assisted diagnostics,” J Med Syst., vol. 46, no. 8, p. 52, 2022.
[3] R. Gillies et al., “Radiomics: Images are more than pictures, they are data,” Radiology, vol. 278, no. 2, pp. 563–577, 2016.
[4] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2015.
[5] G. Litjens et al., “A survey on deep learning in medical image analysis,” Med Image Anal., vol. 42, pp. 60–88, 2017.
[6] M. Frid-Adar et al., “GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification,” Neuro computing, vol. 321, pp. 321–331, 2018.
[7] F. Chollet, “Xception: Deep learning with depthwise separable convo lutions,” in Proc IEEE Conf Comput Vis Pattern Recognit, 2017, pp. 1251–1258.
[8] H. Shin et al., “Deep convolutional neural networks for computer aided detection: CNN architectures, dataset characteristics and transfer learning,” IEEE Trans Med Imaging, vol. 35, no. 5, pp. 1285–1298, 2016.
[9] S. Deepak and P. M. Ameer, “Brain tumor classification using deep CNN features via transfer learning,” Comput Biol Med, vol. 111, p. 103345, 2019.
[10] A. Kumar et al., “Brain tumor classification using SVM and texture features,” IEEE Access, vol. 8, pp. 12345–12356, 2020.
[11] M. Islam et al., “Machine learning-based MRI brain tumor detection,” Comput Biol Med, vol. 129, p. 104142, 2021.
[12] K. He et al., “Deep residual learning for image recognition,” in Proc IEEE Conf Comput Vis Pattern Recognit, 2016, pp. 770–778.
[13] G. Huang et al., “Densely connected convolutional networks,” in Proc IEEE Conf Comput Vis Pattern Recognit, 2017, pp. 2261–2269.
[14] C. Szegedy et al., “Inception-v4, Inception-ResNet and the impact of residual connections on learning,” Proc AAAI Conf Artif Intell, vol. 31, no. 1, pp. 4278–4284, 2017.
[15] M. Tan and Q. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proc Int Conf Mach Learn, 2019, pp. 6105–6114.
[16] I. Goodfellow et al., “Generative adversarial nets,” in Proc Adv Neural Inf Process Syst, 2014, pp. 2672–2680.
[17] H. Chen et al., “GAN-based MRI data augmentation for brain tumor classification,” Med Image Anal, vol. 75, p. 102256, 2022.
[18] S. Alagarsamy et al., “Brain tumor segmentation and classification using deep neural networks,” in 2024 8th Int. Conf. Electron. Commun. Aerosp. Technol. (ICECA), 2024, pp. 1124–1129.
[19] R. Selvaraju et al., “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” in Proc IEEE Int Conf Comput Vis, 2017, pp. 618–626.
[20] S. M. Lundberg and S. I. Lee, “A unified approach to interpreting model predictions,” in Proc Adv Neural Inf Process Syst, 2017, pp. 4765–4774.
[21] B. Hossain et al., “Multi-modal brain tumor classification using deep learning,” Sensors, vol. 23, no. 8, p. 3945, 2023.
[22] M. T. Hayat et al., “A Hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)-Attention Model Architecture for Precise Medical Image Analysis and Disease Diagnosis,” Diagnostics, vol. 15, no. 21, p. 2673, 2025.
[23] C. Guo et al., “On calibration of modern neural networks,” in Proc Int Conf Mach Learn, 2017, pp. 1321–1330.
[24] M. Mashuk et al., “PMRAM Bangladeshi Brain Cancer MRI Dataset,” Kaggle, 2023. [Online]. Available: https://www.kaggle.com/datasets/mashuk/pmram-bangladeshi-braincancer-mri-dataset
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