Alzheimer Disease Detection from MRI Images Using Deep Learning Techniques

Abstract

The early detection of Alzheimer’s disease (AD) is crucial for effective interventionand management. This study explores the application of deep learning techniques toclassify MRI images into three categories: mild demented, moderate demented, andnon-demented. Utilizing a comprehensive preprocessing pipeline, including datanormalization, resizing, histogram equalization, augmentation, dataset balancing, andbatch normalization, we prepared a balanced dataset comprising 800 training images and 200 testing images per class. We implemented and evaluated four deep learningmodels: a modified Convolutional Neural Network (CNN), AlexNet, VGG16, andahybrid model integrating EfficientNet-b0 for feature extraction and Support Vector Machine (SVM) for classification. The performance metrics, including accuracy, precision, recall, F1-score, and support, were determined for each model. Additionally, we analyzed the accuracy and loss curves, confusion matrices, ROC curves, andprecision-recall curves to comprehensively assess the models. The results demonstratethat the hybrid model combining EfficientNet-b0 and SVM outperformed the others with an accuracy of 96.17%, followed by VGG16 with 95.17%, and both the modifiedCNN and AlexNet with 94.83%. These findings suggest the potential of advanceddeep learning architectures in enhancing the diagnostic accuracy for Alzheimer’s disease, thereby supporting early intervention strategies.