Abstract
Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disorder
involving motor neuron degeneration. Significant research has begun to
establish brain magnetic resonance imaging (MRI) as a potential biomarker to
diagnose and monitor the state of the disease. Deep learning has turned into a
prominent class of machine learning programs in computer vision and has been
successfully employed to solve diverse medical image analysis tasks. However,
deep learning-based methods applied to neuroimaging have not achieved superior
performance in ALS patients classification from healthy controls due to having
insignificant structural changes correlated with pathological features.
Therefore, the critical challenge in deep models is to determine useful
discriminative features with limited training data. By exploiting the
long-range relationship of image features, this study introduces a framework
named SF2Former that leverages vision transformer architecture's power to
distinguish the ALS subjects from the control group. To further improve the
network's performance, spatial and frequency domain information are combined
because MRI scans are captured in the frequency domain before being converted
to the spatial domain. The proposed framework is trained with a set of
consecutive coronal 2D slices, which uses the pre-trained weights on ImageNet
by leveraging transfer learning. Finally, a majority voting scheme has been
employed to those coronal slices of a particular subject to produce the final
classification decision. Our proposed architecture has been thoroughly assessed
with multi-modal neuroimaging data using two well-organized versions of the
Canadian ALS Neuroimaging Consortium (CALSNIC) multi-center datasets. The
experimental results demonstrate the superiority of our proposed strategy in
terms of classification accuracy compared with several popular deep
learning-based techniques.