10:00 - 17:00 | Tue 30 Oct | Foyer | B1P-D
We proposed to investigate radiomic features derived from different subcortical brain regions to identify Alzheimer disease (AD), using a random forest classifier model to identify the most important features. Our result suggests that the hippocampus and amygdala are the regions of the brain most different between AD and healthy control (HC) subjects and that “correlation” and “volume” are the most important features for diagnosing AD. Our findings indicated that radiomic features including the histogram, texture and shape features can effectively predict AD patients.
No information added