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Abstract

Brain tumours pose serious health challenges worldwide, demanding early and accurate diagnosis for effective treatment. Traditional diagnostic methods are time-consuming and subject to human error. While deep learning models have advanced automated brain tumour classification, their black-box nature limits clinical trust. This study introduces an ensemble learning framework that integrates EfficientNetB0, InceptionV3, and DenseNet for improved classification accuracy. High-pass filtering (HPF) is employed in the preprocessing phase to enhance tumour-specific features. Three machine learning classifiers—Random Forest, Support Vector Machine, and Logistic Regression—are combined using a soft voting strategy to deliver final predictions. To ensure interpretability, Local Interpretable Model-Agnostic Explanations (LIME) is used to visualise feature contributions. The proposed method achieved a validation accuracy of 97.3% and an AUC-ROC score of 0.9986 on a publicly available brain MRI dataset. These results outperform individual models and enhance clinical reliability. This study demonstrates that combining deep learning, ensemble learning, and XAI techniques can bridge the gap between model performance and interpretability, promoting real-world adoption of AI-based medical diagnosis.

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