A Comparative Analysis of Deep Learning Models for Early Prediction of Alzheimer’s Disease using structural MRI

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and one of the leading causes of dementia worldwide. Early and accurate detection of AD is essential for timely intervention and treatment planning. Magnetic Resonance Imaging (MRI) serves as a non-invasive modality that captures structural brain changes associated with AD progression. In this study, we applied deep learning approaches to classify brain MRI scans into four categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. We evaluated multiple convolutional neural network (CNN) architectures, including VGG-16, Inception-V3, Xception, and EfficientNet-B3, on a publicly available preprocessed MRI dataset containing 6,400 images resized to 128×128 pixels. Experimental results show that EfficientNet-B3 achieved the highest performance with an accuracy of 99.7%, precision of 0.99, recall of 0.99, and F1-score of 0.99. Xception also demonstrated strong performance with an accuracy of 92.5% and F1-score of 0.93, balancing accuracy and inference efficiency. Other models such as VGG-16 and Inception-V3 achieved competitive results but with relatively lower precision and recall. These findings highlight the potential of advanced CNN models, particularly EfficientNet-B3, in enabling automated, accurate, and efficient diagnosis of Alzheimer’s disease. This work contributes to advancing AI-driven tools for clinical decision support and improving the quality of life of patients and caregivers.