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Implementation of an Ensemble Method for Parkinson’s Disease Detection Using MRI Images | ||
| Journal of Data Science and Modeling | ||
| دوره 2، شماره 2 - شماره پیاپی 4، شهریور 2024، صفحه 167-187 | ||
| نوع مقاله: Research Manuscript | ||
| شناسه دیجیتال (DOI): 10.22054/jdsm.2025.79420.1050 | ||
| نویسنده | ||
| Najmeh Jabbari Diziche* | ||
| Allame Tabataba`i university | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Deep Learning؛ Convolutional neural network؛ VGG16؛ ResNet50؛ Magnetic resonance imaging؛ Parkinson's disease | ||
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آمار تعداد مشاهده مقاله: 49 تعداد دریافت فایل اصل مقاله: 26 |
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