Brain Tumor Classification Using a Hybrid Learning Strategy Integrating GAN-Augmented Neural Networks

A Hybrid Perception Network for Accurate and Interpretable Brain Tumor Classification Using Multi-Modal Imaging.

These findings establish HyPerNet as a reliable, interpretable, and computationally efficient solution for clinical brain tumor classification.

Benchmarking Deep Convolutional Neural Networks for Brain Tumor Detection Using Magnetic Resonance Imaging Data

BrainTumor CNN: brain tumor classification via transfer learning and ResNet-18

This study proposes BrainTumor CNN, a convolutional neural network (CNN) for classifying brain tumor MRI images, which leverages transfer learning via a pre-trained ResNet-18 network, integrating data augmentation and Dropout regularization to enhance robustness and generalization.

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

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.

Brain Tumor Classification in MRI Images Using Convolutional Neural Networks with Explainable Artificial Intelligence

Brain tumors are a condition requiring rapid and accurate diagnosis. This study aims to enhance the transparency of Convolutional Neural Network (CNN)-based brain tumor classification models by implementing Explainable Artificial Intelligence (XAI) techniques, specifically Eigen-CAM and LIME. The models were developed using a transfer learning approach on ResNet50 and EfficientNetB0 architectures, utilizing a dataset of 3,000 MRI images categorized into glioma, meningioma, and pituitary tumor classes. Test results indicate that ResNet50 achieved the best performance, with accuracy, precision, recall, and F1-score values of 94%, while EfficientNetB0 achieved 93%. The application of 5-fold cross-validation improved the models' generalization capabilities and reduced the risk of overfitting. Visualizations using Eigen-CAM and LIME demonstrate that the models focus on relevant tumor regions, thereby increasing the transparency and reliability of MRI-based classification.

Accurate Brain Tumor Classification Using MRI Images Based on A Hybrid Vision Transformer and BiLSTM Framework

Results show that ViT–BiLSTM's classification performance is superior to those of traditional deep learning methods: among all the tumor categories its accuracy is higher, its fine-tuning more perfect, as well as, its Recall rates greater.