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

Detection of Alzheimer's Disease using MRI scans based on Inertia Tensor and Machine Learning