Advancing Automated Diagnosis: Convolutional Neural Networks for
Alzheimer’s Disease Classification through MRI Image Processing
Abstract
This study evaluates the performance of a convolutional neural network
(CNN) model for Alzheimer’s disease (AD) classification based on MRI
image processing. The results show that after 22 epochs of training, the
model achieved a validation accuracy of 80.61%. Furthermore, the model
exhibited high precision (78.99%) and recall (30.55%) rates, along
with a significant area under the curve (AUC) value of 86.05%. These
findings suggest the potential of the CNN model in accurately
identifying AD cases using MRI scans, emphasizing its effectiveness as a
diagnostic tool for early detection and intervention in AD.