Building Trust in Artificial Intelligence: An Explainable Deep Learning Framework for Brain Disease Detection
Keywords:
Alzheimer’s disease, Brain Tumor, Convolutional Neural Network, Deep Learning, Explainable AI, Magnetic Resonance Image (MRI), Multiple Sclerosis, Transfer LearningAbstract
Artificial Intelligence (AI) has shown promising results across various research fields. However, there is significant concern about its application in medicine due to the critical need for high accuracy and reliable data in this field. A major issue with many existing machine learning models is their lack of transparency; they do not explain the reasoning behind their outputs. This opacity leads to a lack of trust among healthcare professionals, who are hesitant to rely on such technology for critical decisions. Our research aims to address this concern by developing an Explainable Artificial Intelligence (XAI) model. This model not only classifies MRI images but also provides clear explanations for its predictions. By highlighting the specific regions of the brain that influenced each decision, our XAI model helps bridge the gap between AI and clinical practice. This approach empowers clinicians to identify brain diseases more confidently and accurately, fostering greater trust in AI-driven diagnostic tools.
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