Advances in Deep Learning Techniques for Alzheimer’s Disease Detection Using MRI Images Review
Year: 2024
Type: conference-paper
Abstract: 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.
Author: Abdumannob Muydinov
Institution: Central Asian University
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4411936923