Background:
In this study, we used a convolutional neural network (CNN) to classify
Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects
based on images of the hippocampus region extracted from magnetic resonance (MR) images of
the brain.
Materials and Methods:
The datasets used in this study were obtained from the Alzheimer's Disease Neuroimaging
Initiative (ADNI). To segment the hippocampal region automatically, the patient brain MR
images were matched to the International Consortium for Brain Mapping template (ICBM) using
3D-Slicer software. Using prior knowledge and anatomical annotation label information,
the hippocampal region was automatically extracted from the brain MR images.
Results:
The area of the hippocampus in each image was preprocessed using local entropy minimization
with a bi-cubic spline model (LEMS) by an inhomogeneity intensity correction method.
To train the CNN model, we separated the dataset into three groups, namely AD/NC, AD/MCI,
and MCI/NC. The prediction model achieved an accuracy of 92.3% for AD/NC, 85.6% for
AD/MCI, and 78.1% for MCI/NC.
Conclusion:
The results of this study were compared to those of previous studies, and summarized
and analyzed to facilitate more flexible analyses based on additional experiments. The classification
accuracy obtained by the proposed method is highly accurate. These findings suggest
that this approach is efficient and may be a promising strategy to obtain good AD, MCI and
NC classification performance using small patch images of hippocampus instead of whole slide
images.
Multiplane Convolutional Neural Network (Mp-CNN) for Alzheimer’s Disease Classification
