Early Diagnosis of Parkinson’s Disease in brain MRI using Deep Learning Algorithm

The Parkinson's disease (PD) is one of the top most prevalent degenerative disease which is caused by the loss of neurons that produce dopamine. Magnetic Resonance Imaging (MRI) is capable of capturing changes in the structure of the brain caused due to deficiency of dopamine in subjects of Parkinson's disease. Early diagnosis of these type of diseases using computer-aided systems is an area of eminent importance and extensive research amongst researchers. Deep learning models can effectively assist the clinicians in the PD diagnosis and obtain an objective patient group classification in coming years. In this paper, detection of PD is done using deep learning algorithm to discriminate between PD and controlled subjects, which is difficult and time taking if done manually. According to research, the chance of curing increases significantly if appropriate steps are taken early and precious time could be saved if detection process is carried by a computer. By making use of the Convolutional Neural Network (CNN) and the LeNet-5 architecture, the MRI data of PD subjects was successfully classified from normal controls.