Revolutionizing Alzheimer’s Disease Diagnosis using Deep Learning Approach

Abstract

Abstract: Disorders of the brain are one of the most difficult diseases to cure because of their fragility, the difficulty of performing procedures, and the high costs. On the other hand, the surgery itself does not have to be effective because the results are uncertain. Adults who have hypertension, one of the most common brain illnesses, may have different degrees of memory problems and forgetfulness. Depending on each patient's situation. For these reasons, it's crucial to define memory loss, determine the patient's level of decline, and determine his brain MRI scans are used to identify Alzheimer's disease. In this project, we discuss methods and approaches for diagnosing Alzheimer's disease using deep learning. The suggested approach is utilized to enhance patient care, lower expenses, and enable quick and accurate analysis in sizable investigations. Alzheimer's disease is a progressive neurodegenerative disorder that affects millions of people worldwide. Early detection of the disease can improve patient outcomes, and brain MRI scans have shown promise as a tool for detecting Alzheimer's disease in its early stages. In recent years, deep learning algorithms, such as convolutional neural networks (CNNs), have been increasingly used in Alzheimer's disease analysis from brain MRI scans. This paper proposes a CNN-based system for Alzheimer's disease analysis from brain MRI scans. The proposed system involves several steps, including data preprocessing, feature extraction, training the CNN model, and evaluating its performance on a test set. The results demonstrate the effectiveness of the proposed CNN-based system in accurately detecting Alzheimer's disease from brain MRI scans. The proposed system has the potential to improve early detection and monitoring of Alzheimer's disease, leading to improved patient outcomes.