Document Type : Research Manuscript

Author

• Najmeh Jabbari Diziche

Allame Tabataba`i university

Abstract

Parkinson's disease (PD) is a common neurological disorder that has a significant impact on the elderly population worldwide. This study investigates the use of deep learning models, including VGG16, ResNet50, and a simple CNN, in classifying MRI images to distinguish between Parkinson's patients and normal subjects. The relevant data includes 610 normal subjects and 221 Parkinson subjects. Using ensemble learning techniques with support vector machine (SVM) as a sub-trainer, our model achieved 96% classification accuracy. Applying various hybrid methods such as majority vote, weighted average, and weighted majority vote on the outputs of base learning models helped us achieve a much more improved performance and reduce variability in classification results. These findings promise progress in the accurate diagnosis of Parkinson's disease using deep learning methods in medical imaging. To confirm the practicality of the attained results of the proposed diagnostic approach, further multicenter studies with larger patient groups are recommended.

Keywords

• Deep Learning
• Convolutional neural network
• VGG16
• ResNet50
• Magnetic resonance imaging
• Parkinson's disease

Main Subjects

• Neural Network