MRI Based Early Detection of Parkinson\'s Disease

Abstract

Abstract: Parkinson’s disease (PD) is a neurological disorder that causes the loss of neurons that produce dopamine in the brain. Dopamine is a chemical messenger that regulates movement and other functions. The exact causes of PD are unknown, but some genes, environmental factors, and triggers may play a role. People with PD may have symptoms for many years before they are diagnosed. PD affects 7–10 million people worldwide and causes various motor and cognitive impairments. Researchers have used different methods to diagnose PD, such as speech analysis and machine learning. However, these methods have limitations, such as low accuracy and inability to handle large data. This study proposes a new method that uses deep learning to predict PD in its early stages based on MRI scans. The method will classify MRI samples into PD and non-PD groups using a convolutional neural network (CNN) algorithm. It will also compare the performance of different MRI modalities in distinguishing PD from normal aging. CNN is suitable for this task because it can learn complex patterns from MRI data. The proposed method will provide a reliable and efficient way to detect PD in its early stages and help patients receive appropriate medical care and medications that can improve their quality of life.