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A Robust ConvNeXt-Based Framework for Efficient, Generalizable, and Explainable Brain Tumor Classification on MRI
Kirti Pant1, Pijush Kanti Dutta Pramanik2, Zhongming Zhao3
1Department of Computer Science and Engineering, Bipin Tripathi Kumaon Institute of Technology, Dwarahat 263653, Uttarakhand, India.
Bioengineering (Basel, Switzerland)
|February 27, 2026
Summary
This study introduces ConvNeXt Base for accurate brain tumor classification from MRI scans. The model demonstrates high diagnostic accuracy, generalizability, and explainability, making it suitable for clinical use.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Biology
Background:
- Accurate brain tumor classification from MRI is crucial but challenging due to data variability and model generalization issues.
- Current deep learning models often lack robust validation and interpretability, limiting clinical reliability.
- Existing methods struggle with diverse tumor appearances and inter-dataset variability.
Purpose of the Study:
- To develop and evaluate a robust brain tumor classification framework using the ConvNeXt Base architecture.
- To assess the model's performance across multiple independent MRI datasets for glioma, meningioma, and pituitary tumors.
- To ensure the model's reliability, generalizability, and clinical applicability through rigorous validation and interpretability analysis.
Main Methods:
- Utilized the ConvNeXt Base architecture for brain tumor classification on three independent MRI datasets.
- Evaluated performance using comprehensive metrics (accuracy, AUC, F1-score, etc.) and statistical validation (Friedman test, Holm-Bonferroni).
- Assessed model interpretability using Grad-CAM++ and Gradient SHAP, alongside computational efficiency analysis.
Main Results:
- ConvNeXt Base achieved near-perfect classification performance (accuracy >99.6%, AUC ≈1.0) across all datasets.
- Statistical analyses confirmed significant and reproducible performance gains over other architectures.
- Explainability methods confirmed predictions are based on tumor-relevant regions, with favorable inference speed and resource usage.
Conclusions:
- ConvNeXt Base offers a reliable, generalizable, and explainable solution for MRI-based brain tumor classification.
- The model's high diagnostic accuracy and statistical robustness support its integration into clinical workflows.
- The framework's computational efficiency and interpretability enhance its potential for real-world clinical applications.
