Abstract:In the world, about seven to ten million elderly people are suffering from the Parkinson's Disease (PD). PD is a common degenerative nervous system disease. Its clinical characters are tremor, muscle rigidity, bradykinesia, and the degression of independent ability. These characters are similar with the Multiple System Atrophy (MSA). Research shows that patients with PD are often irreparably diagnosed, so people are constantly exploring new ways to differentiate PD with MSA and get early diagnosis. With the advent of the big data era, deep learning has made major breakthroughs in image recognition and classification. Therefore, the study uses the deep learning methods to differentiate PD, MSA, and healthy people. The data is from 301 Hospital of Beijing. The pre-treatment of the original Magnetic Resonance Image (MRI) is directed by the physicians of 301 Hospital of Beijing. The focus of this experiment is to optimize the neural network and make it get good results in medical image recognition and diagnosis. Based on the pathological characteristics of PD, the study proposed an improved algorithm, and it gets the better experimental results in loss, accuracy, and other indicators.