  Alzheimer's Disease is a devastating neurological disorder that is
increasingly affecting the elderly population. Early and accurate detection of
Alzheimer's is crucial for providing effective treatment and support for
patients and their families. In this study, we present a novel approach for
detecting four different stages of Alzheimer's disease from MRI scan images
based on inertia tensor analysis and machine learning. From each available MRI
scan image for different classes of Dementia, we first compute a very simple 2
x 2 matrix, using the techniques of forming a moment of inertia tensor, which
is largely used in different physical problems. Using the properties of the
obtained inertia tensor and their eigenvalues, along with some other machine
learning techniques, we were able to significantly classify the different types
of Dementia. This process provides a new and unique approach to identifying and
classifying different types of images using machine learning, with a
classification accuracy of (90%) achieved. Our proposed method not only has the
potential to be more cost-effective than current methods but also provides a
new physical insight into the disease by reducing the dimension of the image
matrix. The results of our study highlight the potential of this approach for
advancing the field of Alzheimer's disease detection and improving patient
outcomes.

Image and Video Processing;Neural and Evolutionary Computing