A novel CNN architecture for accurate early detection and classification of Alzheimer’s disease using MRI data
Abstract Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder that requires accurate diagnosis for effective management and treatment. In this article, we propose an architecture for a convolutional neural network (CNN) that utilizes magnetic resonance imaging (MRI) data from the Al...
Main Authors: | A. M. El-Assy, Hanan M. Amer, H. M. Ibrahim, M. A. Mohamed |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2024-02-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-024-53733-6 |
Similar Items
-
Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP
by: Mohan Bhandari, et al.
Published: (2023-02-01) -
Explaining graph convolutional network predictions for clinicians—An explainable AI approach to Alzheimer's disease classification
by: Sule Tekkesinoglu, et al.
Published: (2024-01-01) -
Deep Multi-Branch CNN Architecture for Early Alzheimer’s Detection from Brain MRIs
by: Paul K. Mandal, et al.
Published: (2023-09-01) -
Lung Sound Classification With Multi-Feature Integration Utilizing Lightweight CNN Model
by: Thinira Wanasinghe, et al.
Published: (2024-01-01) -
TriAD: A Deep Ensemble Network for Alzheimer Classification and Localization
by: Francesco Mercaldo, et al.
Published: (2023-01-01)