ABSTRACT Parkinson Disease is one of the most widely recognized neurodegenerative disorders. In the U.S. Parkinson disease prevalence is roughly 20 cases for every 100,000 people per year, with the mean period of beginning near 60 years. Thus, building up an automatic system for detecting parkinson would be gainful for treating the infection without any delay especially in remote areas. Due to the accomplishment of profound learning calculations in breaking down clinical images, Convolution Neural Networks (CNNs) have increased a lot of consideration for medical disease classification. What's more, highlights realized by pre-prepared CNN models on huge scale datasets are a lot of valuable in picture characterization errands. In this work, we evaluate the functionality of pre-prepared CNN models used as highlight extractors followed by different classifiers for the order of anomalous and normal MRI check pictures.