Stacked CNN Architectures for Robust Brain Tumor MRI Classification

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Abstract

Brain tumor classification using MRI scans is crucial for early diagnosis and treatment planning. In this study, we first train a single Convolutional Neural Network (CNN) based on VGG16 [1], achieving a strong standalone test accuracy of 99.24% on a balanced dataset of 7,023 MRI images across four classes: glioma, meningioma, pituitary, and no tumor. To further improve classification performance, we implement three ensemble strategies: stacking, soft voting, and XGBoost-based ensembling [4], each trained on individually fine-tuned models. These ensemble methods significantly enhance prediction accuracy, with XGBoost achieving a perfect 100% accuracy, and voting reaching 99.54%. Evaluation metrics such as precision, recall, and F1-score confirm the robustness of the approach. This work demonstrates the power of combining fine-tuned deep learning models [5] for highly reliable brain tumor classification enhance prediction accuracy, with XGBoost achieving a perfect 100% accuracy, and voting reaching 99.54%. Evaluation metrics such as precision, recall, and F1-score confirm the robustness of the approach. This work demonstrates the power of combining fine-tuned deep learning models for highly reliable brain tumor classification.

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