A System for Diagnosing Alzheimer’s Disease from Brain MRI Images Using Deep Learning Algorithm

In addition to their vulnerability, the complexity of the operations, and the high expenses, disorders of the brain are one of the most challenging diseases to treat. However, because the outcome is unpredictable, the procedure itself does not need to be successful. One of the most prevalent brain diseases in adults, hypertension, can cause varying degrees of memory loss 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 thesis, 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. Modern deep learning techniques have lately successfully demonstrated performance at the level of a human in various domains, including medical image processing. We propose a deep convolutional network for diagnosing Alzheimer's disease based on the analysis of brain MRI data. Our model outperforms other models for early detection of current techniques because it can distinguish between different stages of Alzheimer's disease.