Dementia Disease Progression Analysis from 2D Cellular Automata Architecture

The detection of change in mild cognitive impairment (MCI) is vital between one visit and another visit for the early-stage detection of Alzheimer's disease (AD).It is associated with changes in the structural and functional aspects.Diagnosis of dementia progression is a challenging but essential step for the planning of early treatment.This paper proposes the cellular automata (CA) framework to identify the changes when the disease progresses from one visit to another.An updated rule using the conduction function of anisotropic diffusion has been proposed to assess the earliest signs of dementia manifestation and progression.The progression of dementia disease in multi-visit subjects for healthy and unhealthy patients has been analyzed by examining the entropy from evolved 2D CA images.The subjects below the reference line in healthy cases lead to the prognosis of the disease, whereas subjects above the reference line in unhealthy subjects are in improved condition.The proposed CA architecture shows the transitions and progressive patterns, thereby offering a method for tracking the evolution of dementia over time.