Abstract:
"
Alzheimer’s Disease is one of the most common cause of dementia with ten million new cases
every year. Early detection of the disease is being crucial thus, medical professionals can begin
treatment to slow or halt the progression of the disease. Due to the criticality of the disease,
accurate and precise diagnosis results are being a necessity. This dissertation aims to provide a
system which automates the diagnosis process of the disease using neuroimaging biomarker
mainly magnetic resonance imaging which has proven to be one of the earliest biomarkers used to
diagnose the disease.
This dissertation proposes and implements a novel approach to the problem in a quantum machine
learning perspective. Quantum machine learning uses quantum mechanical properties such as
super-position and entanglement to process data. Which is believed to outperform classical
computing in processing quantum like data. We compare different quantum machine learning
approaches and classical algorithms to select a suitable approach to automate the diagnosis
process. The implemented system outperforms several state-of-the-art classical systems which
shows promising application of quantum machine learning in disease diagnosis and neuroimaging."
