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This paper presents a deep learning-based model developed to accurately classify Parkinson's disease patients using 3D MRI scans and data augmentation techniques. The study aimed to address the research question of whether this approach could effectively detect structural changes indicative of Parkinson's disease and enhance diagnostic accuracy. The model utilized 3D brain MRI scans from the midbrain region and leveraged the domain of data augmentation to expand the dataset and improve generalizability. The testing accuracy of the model was evaluated with and without data augmentation to further understand it's applicability. The paper demonstrated the effectiveness of the proposed model in classifying Parkinson's disease patients from healthy controls, achieving a testing accuracy of up to 90.13% with data augmentation. The incorporation of data augmentation led to a significant 15.53% increase …