<![CDATA[Alzheimer’s disease is a progressive brain disorder that causes memory loss and cognitive decline, affecting millions of people worldwide. Early detection is critical for slowing the disease's progression and improving patient outcomes. Magnetic Resonance Imaging (MRI) is widely used to identify brain changes associated with AD, but subtle abnormalities in the early stages are often difficult to detect using traditional methods. In this study, we used a deep learning approach with a model called ResNet-50 to analyze MRI scans and classify patients into four categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. The model was trained using MRI images, achieving an accuracy of 95.63%, with strong sensitivity, precision, and specificity. To make the model’s predictions understandable for healthcare professionals, we applied a technique called Grad-CAM, which highlights areas of the brain that influenced the model’s decisions. These visual explanations help clinicians see and trust the reasoning behind the AI's results. While the model performed well overall, misclassifications between adjacent disease stages were observed, likely due to class imbalance and subtle brain changes. This study demonstrates that explainable AI tools can improve early detection of Alzheimer’s disease, supporting clinicians in making accurate and timely diagnoses. Future work will focus on expanding the dataset and combining MRI with other clinical information to enhance the tool's reliability in real-world settings.]]>
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