An Explainable Machine Learning Model for Early Detection of Brain Tumors: Integrating Multi-Modal Medical Imaging and Intelligent Feature Fusion
Received: 16 April 2025 | Revised: 12 May 2025, 2 June 2025, and 10 June 2025 | Accepted: 14 June 2025 | Online: 31 July 2025
Corresponding author: Banu S.
Abstract
The early diagnosis of Brain Tumors (BT) is a critical challenge in medical imaging. This study proposes an explainable machine learning (XAI) framework that integrates multimodal imaging, including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), for accurate and interpretable BT detection. A hybrid feature extraction strategy was employed, combining deep learning-based spatial features with handcrafted texture descriptors, including GLCM and LBP. These features are fused using an attention-based mechanism to enhance discriminative performance. The refined features are classified using an ensemble of Random Forest, XGBoost, and Deep Neural Networks. Explainability is incorporated using SHAP and Grad-CAM to visualize the model's decision rationale. Experiments on publicly available datasets demonstrate superior performance, achieving 97.3% accuracy, 96.4% precision, 96.0% recall, and 96.2% F1-score, outperforming existing methods while ensuring clinical interpretability.
Keywords:
explainable machine learning, brain tumor, classification, feature fusion, multimodal, accuracy, neural networkDownloads
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Copyright (c) 2025 S. Rukmani Devi, J. Jean Justus, M. Vanathi, Bobba Veeramallu, V. Aruna, T. C. Manjunath, Valisher Sapayev Odilbek Uglu, S. Banu
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