Advances in Deep Learning Techniques for Alzheimer’s Disease Detection Using MRI Images Review

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions around the world.Early and accurate detection is crucial for effective treatment and management.Recent advances in deep learning, particularly convolutional neural networks (CNNs), have shown promise in improving the diagnostic accuracy of AD using MRI images.This paper reviews and compares four recent studies that utilize different deep learning architectures: InceptionResNetV2, ResNet50, VGG19, DenseNet-201 and EfficientNetB7 for the classification of AD stages using MRI data.We analyze their methodologies, data sets, and performance metrics and discuss the advantages and limitations of each approach.Our findings indicate that advanced architectures like DenseNet-201 and EfficientNetB7 achieve higher accuracy in multiclass classification tasks, highlighting the potential of deep learning in improving AD diagnosis.