3D Brain MRI Classification for Alzheimer Diagnosis Using CNN with Data Augmentation
Journal:
arXiv
Published Date:
May 7, 2025
Abstract
A three-dimensional convolutional neural network was developed to classify
T1-weighted brain MRI scans as healthy or Alzheimer. The network comprises 3D
convolution, pooling, batch normalization, dense ReLU layers, and a sigmoid
output. Using stochastic noise injection and five-fold cross-validation, the
model achieved test set accuracy of 0.912 and area under the ROC curve of
0.961, an improvement of approximately 0.027 over resizing alone. Sensitivity
and specificity both exceeded 0.90. These results align with prior work
reporting up to 0.10 gain via synthetic augmentation. The findings demonstrate
the effectiveness of simple augmentation for 3D MRI classification and motivate
future exploration of advanced augmentation methods and architectures such as
3D U-Net and vision transformers.
