Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject.