Determination of Alzheimer's disease based on morphology and atrophy using machine learning combined with automated segmentation
Natsuki Ikemitsu1, Yuki Kanazawa2, Akihiro Haga2, Hiroaki Hayashi3, Yuki Matsumoto2, and Masafumi Harada2
1Graduate school of Health Science, Tokushima University, Tokushima, Japan, 2Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan, 3Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan
To classify healthy subjects or patients with Alzheimer's disease (AD) using three-dimensional T1w data, we developed a machine learning system which can capture morphology features and determine atrophy of brain tissue in early-stage AD. Deep learning, a support vector machine (SVM), and 3D convolutional neural networks (3DCNN) were performed. The accuracies of SVM and deep learning based on volume values were comparable and greatly exceeded the accuracy of 3DCNN. It was found that atrophic features were more considerable than morphological features in early-stage AD.
This abstract and the presentation materials are available to members only;
a login is required.