Parkinson’s Disease Classification based on Enhanced Ensemble Learning and Brain MRI

Parkinson's Disease (PD) is a neurological disorder that causes progressive loss of brain cells. Despite the fact that there is no known cure for this neurodegenerative disease at present, early diagnosis and treatment may improve the quality of life. Magnetic Resonance Imaging (MRI) detects structural changes related to dopamine deficiency in PD. To categorize MRI scans as Healthy Control (HC) or PD, this study proposes an ensemble of Deep Convolution Neural Network (DCNN) models. Initially, we have used DCNN models using augmentation and transfer learning, to classify as PD or HC. In the next stage, we applied a classifier fusion ensemble approach to enhance the overall result of the classification model. The proposed model is trained using the data collected from PPMI database, while assessed on custom dataset which is created using the data collected from Lalitha Super Specialty Hospital (LSSH). Finally, it was observed that the developed ensemble model produced an outstanding performance by plotting an overall accuracy of 99%, while the transfer learned EfficientNet B1 DCNN model stood in the second position, achieving a remarkable accuracy of 98%. This study serves as a significant step forward, providing valuable insights for researchers and clinicians engaged in the domain of clinical image assessment using deep learning techniques.