Paper Contents
Abstract
This project presents an integrated approach to assist in early diagnosis and treatment simulation of Alzheimer's Disease. Using a Convolutional Neural Network (CNN), MRI images are classified into four stages: Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Alzheimers Disease (AD). Furthermore, a Reinforcement Learning (RL) model is introduced to simulate disease progression under various treatment strategies using a Q-learning agent. The agent learns an optimal policy to delay disease progression. Experimental results show promising stage-wise classification performance and strategic treatment action selection using learned Q-values.
Copyright
Copyright © 2025 Ramya B N. This is an open access article distributed under the Creative Commons Attribution License.
