A Machine Learning-Based Early Diagnosis of Alzheimer’s Disease
Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases in the world. Though there is no cure, an early detection of AD (when signs of mild cognitive impairment (MCI) are shown) may be critical to allow for treatment that may further prolong the patient’s brain function. In this study, Jack presents a generalized classification model using a three-dimensional DenseNet architecture for AD diagnosis through whole-brain magnetic resonance imaging. He then evaluated his classifier on two different datasets to assess both its performance as well as the validity of MCI as a precursor stage for AD. Jack found that his model achieved high accuracies upwards of 80%, indicating that the classifier succeeded in providing a preliminary diagnosis of AD from magnetic resonance imaging.
Jack wrote a paper on his findings.