Two Birds with One Stone: A Dense Attentive GAN-based Model for Detecting Alzheimer's and Parkinson's Disorders

This paper investigates two neurodegenerative disorders in adults -- Alzheimer's (AD) and Parkinson's (PD). Most works in the research explored these diseases independently - AD vs. healthy subjects and PD vs. healthy subjects. We consider a one-class framework for capturing the characteristics of healthy subjects. This one-class model views any subject with AD or PD as an outlier. We employ Dense Attentive GAN for the one-class model. T1-weighted longitudinal structural magnetic resonance images (sMRI) are used as input. Furthermore, we use longitudinal samples to train the framework (two scans per subject over a 12-month gap). Most traditional methods relied on cross-sectional data (one scan per subject). The proposed method is analogous to doctor's diagnoses of the subject in different sessions to recognise the disorder. Extensive experimental studies have shown that our proposed method outperforms all other AD and PD-based works in the literature.