The content discusses the integration of Explainable Artificial Intelligence (XAI) techniques with Artificial Intelligence of Medical Things (AIoMT) to improve healthcare systems, specifically in brain tumor detection. The proposed framework utilizes Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-CAM) to enhance decision-making processes. Evaluation results demonstrate high precision, recall, and F1 scores, showcasing the effectiveness of the XAI framework in diagnosing brain tumors accurately.
The study addresses challenges in brain tumor diagnosis due to non-specific symptoms and imaging characteristics. It proposes an XAI framework tailored for AIoMT to improve patient outcomes and autonomous diagnosis. The research integrates maximum voting classifiers with edge cloud-driven training for reliable diagnoses. Customized XAI techniques like SHAP, LIME, and Grad-CAM ensure transparent and interpretable decisions in medical applications.
Furthermore, the content explores mathematical formulations for ensemble models using a majority voting classifier technique. It highlights the importance of explainability through XAI methods like LIME, SHAP, and Grad-CAM in identifying brain tumors accurately. The proposed ensemble model achieves high accuracy rates with training accuracy at 99% and validation accuracy at 98%.
Overall, the study emphasizes the significance of integrating advanced XAI techniques with deep learning methodologies for precise and reliable brain tumor diagnoses within AIoMT applications.
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arxiv.org
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by Al Amin,Kamr... о arxiv.org 03-08-2024
https://arxiv.org/pdf/2403.04130.pdfГлибші Запити