Comparative evaluation and interpretability analysis of modern CNN architectures for brain tumor MRI classification

Background:Accurate and interpretable brain tumor classification from MRI images remains a key challenge in medical image analysis, particularly when using publicly available datasets of moderate size. Objectives:This study investigates the performance of a ConvNeXt-Tiny based framework for four-class brain tumor classification glioma, meningioma, pituitary tumor, and no tumor and compares it with established convolutional architectures. Materials and methods:Using transfer learning and identical experimental settings, ConvNeXt-Tiny was evaluated against DenseNet169, Xception, MobileNetV3-Large, CNN+DenseNet169, and ResNet50. Standard evaluation metrics (accuracy, precision, recall, and F1-score) were used, and Grad-CAM was applied to visualize model attention for interpretability. Generalization was further assessed using an independent dataset. Results:ConvNeXt-Tiny achieved high overall performance (accuracy = 0.9924, F1-score = 0.9918), comparable to DenseNet169 and Xception but with lower computational cost. The model maintained stable learning behavior, minimal overfitting, and consistent accuracy on unseen data. Grad-CAM visualizations confirmed that the network focused on clinically relevant tumor regions, improving transparency and reliability of predictions. Conclusion:ConvNeXt-Tiny provides a strong and efficient baseline for interpretable brain tumor classification, balancing accuracy and computational efficiency. While the results are promising, differences among recent architectures were modest, and clinical validation using multi-center MRI datasets is necessary to confirm broader applicability.