Detecting Alzheimer’s disease in MRI using deep learning

• Main Authors: Gupta, Prajwal, Srinivasan, Aditya, Revathi, M.
• Format: Conference Proceeding
• Language: English
• Subjects: Deep learning; Image enhancement; Image quality; Magnetic resonance imaging; Signs and symptoms
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0217183

To format your Alzheimer’s disease (AD) is a progressive disorder that causes difficult in language and decision making, memory loss and a decline in other cognitive abilities. Since there is no definite cure for this, diagnosing the disease in an early stage is the only prevention. But detecting this disease in its early stages is one of the most difficult and challenging aspect as the symptoms of the early stages may start very early in life and are very subtle. Hence, there are instances where the disease isn’t caught until it is in its final stages. There have been factors found like genetics, age and lifestyle choice that can contribute and even increase the chance of developing the disease. It’s also critical to understand that detecting and treating AD can ease symptoms and enhance the patient’s overall quality of life. In this study, our main objective is to detect if a person has AD using the Magnetic Resonance Images (MRIs) and at which stage it is by exploring the applications of deep learning. One more objective of our study is to find out if changing the color scheme of MRIs has any effect on the performance of the initial goal. The present study successfully accomplished its objectives, as evidenced by the high accuracy attained by the proposed approaches in addressing the problem statement.