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Parkinson’s disease (PD) is one of the main types of neurological disorders affected by progressive brain degeneration. Early detection and prior care may help patients to improve their quality of life, although this neurodegenerative disease has no known cure. Magnetic Resonance (MR) Imaging is capable of detecting the structural changes in the brain due to dopamine deficiency in Parkinson’s disease subjects. Deep learning algorithms provide cutting-edge results for various machine learning and computer vision tasks. We have proposed an approach to classify MR images of healthy and Parkinson’s disease patients using deep convolution neural network. However, these algorithms require a large training dataset to perform well on a particular task. To this effect, we have applied a deep convolution neural network classifier that incorporates transfer learning and data augmentation techniques to …