  Deep learning has great potential for accurate detection and classification
of diseases with medical imaging data, but the performance is often limited by
the number of training datasets and memory requirements. In addition, many deep
learning models are considered a "black-box," thereby often limiting their
adoption in clinical applications. To address this, we present a successive
subspace learning model, termed VoxelHop, for accurate classification of
Amyotrophic Lateral Sclerosis (ALS) using T2-weighted structural MRI data.
Compared with popular convolutional neural network (CNN) architectures,
VoxelHop has modular and transparent structures with fewer parameters without
any backpropagation, so it is well-suited to small dataset size and 3D imaging
data. Our VoxelHop has four key components, including (1) sequential expansion
of near-to-far neighborhood for multi-channel 3D data; (2) subspace
approximation for unsupervised dimension reduction; (3) label-assisted
regression for supervised dimension reduction; and (4) concatenation of
features and classification between controls and patients. Our experimental
results demonstrate that our framework using a total of 20 controls and 26
patients achieves an accuracy of 93.48$\%$ and an AUC score of 0.9394 in
differentiating patients from controls, even with a relatively small number of
datasets, showing its robustness and effectiveness. Our thorough evaluations
also show its validity and superiority to the state-of-the-art 3D CNN
classification methods. Our framework can easily be generalized to other
classification tasks using different imaging modalities.
