August 15, 2025Open Access

Stacked CNN Architectures for Robust Brain Tumor MRI Classification

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Ensemble techniques significantly improve MRI-based brain tumor classification accuracy, suggesting effective deep learning strategies.

Classification accuracy improved to 100% with XGBoost, demonstrating the effectiveness of ensemble methods in deep learning.
The single CNN model based on VGG16 achieved a strong test accuracy of 99.24% on 7,023 MRI images across four tumor classes.
Stacking and voting ensemble techniques were implemented, enhancing predictive performance beyond standalone models.
Evaluation metrics, including precision and F1-score, confirm the robustness of the combined deep learning approach.

Alireza Rahi (2025) studied this question.

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