Alzheimer's Disease Prediction Using Deep Learning

This paper addresses the challenge presented by Alzheimer's disease,which is a progressive brain disorder known for its detrimental effects on memory, cognition, and daily functioning, ultimately compromising patient independence. Given its status as the leading cause of dementia in the elderly, there is an urgent need for accurate and early diagnosis, particularly in light of projected increases in caregiving costs. By leveraging MRI data from the Open Access Series of Imaging Studies (OASIS), this study mainly focuses on harnessing deep learning techniques, notably Convolutional Neural Networks (CNNs), to achieve precise detection and staging of Alzheimer's disease across its various stages, including Mild Dementia, Moderate Dementia, Very Mild Dementia, and Non-Dementia. By enabling timely diagnosis, the proposed approach facilitates access to early interventions, such as medical therapies and lifestyle adjustments, crucial for mitigating disease progression and enhancing patients' quality of life. Through the accurate classification of these distinct disease stages, healthcare professionals can intervene proactively, offering more effective treatments and support to both individuals affected by Alzheimer's and their families.