Detection of Alzheimer's Disease Through Convolutional Neural Networks: An Analysis of Magnetic Resonance Imaging Images

Alzheimer's disease is a progressive ailment characterized by progressive deterioration of cognitive function, typically observed in the senior citizen population. This condition presents a substantial worldwide health concern. The timely identification and intervention have a key role in enhancing the overall well-being of persons who are impacted. This research paper introduces a Convolutional Neural Network (CNN) architecture designed for the purpose of classifying Alzheimer's disease based on brain MRI scans. The CNN model that has been presented utilizes deep learning capabilities to autonomously extract pertinent information from brain MRI scans, hence allowing the precise categorization of Alzheimer's disease. The model has a notable level of classification accuracy and displays promising prospects for timely identification and action. By presenting the possibility of enhancing patient outcomes and quality of life, this research adds to the continuing attempts to develop automated and non-invasive diagnostic techniques for Alzheimer's disease.