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Early Prediction of Neurodegenerative Diseases Using Deep Learning (2019-2023)

Project

Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) are the two most common neurodegenerative diseases caused by structural changes in the brain and lead to deterioration of cognitive functions. Patients usually experience diagnostic symptoms at later stages after irreversible neural damage occurs. Early detection of such diseases is crucial in maximizing patients’ quality of life and to start treatments to decelerate the progress of the disease. Early detection may be possible via computer-assisted systems using neuro-imaging data. Among all, deep learning utilizing magnetic resonance imaging (MRI) have become a prominent tool due to its capability to extract high-level features through local connectivity, weight sharing, and spatial invariance. This project investigates the detection of AD and PD by building various 2D and 3D convolutional models.

A 3D convolutional neural network (CNN) architecture for disease classification. 3D boxes show input and feature maps.

Project members

Team members

Acknowledgements

This project has received funding from the School of Computer Science and Electrical Engineering (CSEE) PhD Scholarship Programme.