Pretrained Sequential CNN Model for Alzheimer Disease Prediction Using MRI Images

Alzheimer's disease is a neurodegenerative disorder affecting brain function, characterized by progressive loss of intellect, memory, and other behaviors through multiple stages of what is termed dementia. Therapy intervention and overall condition management require early screening of the disease. MRI as a part of medical imaging is invaluable when it comes to understanding both normal and diseased brain anatomy. Ideally, Alzheimer's disease is a chronic disease that worsens with time and hence calls for regular timely treatment. Four classes of the patients were used in the study; Mildly Demented, Moderately Demented, Non-Demented, Very Mildly Demented to develop a sequential CNN model to detect Alzheimer's diseases. Separating these different stages of Alzheimer's disease demonstrates impressive specificity of the model at 94.84%, meaning early stages can be detected, and proper prevention begun. To identify the Alzheimer's disease stages based on medical imaging data, the proposed sequential CNN model of this work After work presents high accuracy. This paradigm appears well suited for enhancing patient outcomes in Alzheimer's disease by increasing the possibility of individualized therapeutic strategies, enhancing early diagnosis, and integrating into clinical practice when validated further.