  Structural MRI(S-MRI) is one of the most versatile imaging modality that
revolutionized the anatomical study of brain in past decades. The corpus
callosum (CC) is the principal white matter fibre tract, enabling all kinds of
inter-hemispheric communication. Thus, subtle changes in CC might be associated
with various neurological disorders. The present work proposes the potential of
YOLOv5-based CC detection framework to differentiate atypical Parkinsonian
disorders (PD) from healthy controls (HC). With 3 rounds of hold-out
validation, mean classification accuracy of 92% is obtained using the proposed
method on a proprietary dataset consisting of 20 healthy subjects and 20 cases
of APDs, with an improvement of 5% over SOTA methods (CC morphometry and visual
texture analysis) that used the same dataset. Subsequently, in order to
incorporate the explainability of YOLO predictions, Eigen CAM based heatmap is
generated for identifying the most important sub-region in CC that leads to the
classification. The result of Eigen CAM showed CC mid-body as the most
distinguishable sub-region in classifying APDs and HC, which is in-line with
SOTA methodologies and the current prevalent understanding in medicine.
