Alzheimer’s disease is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and behavioral impairment. Early diagnosis remains a critical challenge due to the subtle onset of symptoms and the reliance on expert-driven analysis of medical imaging. In recent years, artificial intelligence (AI), particularly deep learning, has shown significant potential in improving diagnostic accuracy and enabling early-stage detection. This paper presents an AI-driven framework for the early prediction of Alzheimer’s disease using brain Magnetic Resonance Imaging (MRI). The core of the proposed system is a Convolutional Neural Network (CNN) model trained on a large dataset of labeled MRI scans. The model is designed to automatically extract discriminative features from brain images and classify them into four stages: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. To enhance model robustness and generalization, preprocessing techniques such as normalization, resizing, and data augmentation were applied. The framework further extends the role of AI beyond diagnosis through AI-powered interaction and patient support features. The overall system highlights the effectiveness of combining deep learning-based medical image analysis with intelligent healthcare applications. The results indicate that AI can play a transformative role in assisting clinicians, improving early detection accuracy, and enhancing decision-making in neurodegenerative disease diagnosis while contributing to accessible, scalable, and user-centered healthcare solutions.
AbdelFattah et al. (Sun,) studied this question.
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