Classifying Parkinson’s Disease Patients from Healthy Controls Using a ResNet18 Convolutional Neural Network Model of T1-Weighted MRI
Ahmed Negida1, Ahmed Azzam2, Ibrahim Serag3, Amr Hassan4, Rehab Diab5, Mohamed Diab6, Mahmoud Hefnawy7, Mohammed Ahmed Ali8, Brian Berman1, Matthew Barrett1
1Virginia Commonwealth University, 2October 6 University, 3Mansoura University, 4University of California, 5Al-Azhar University, 6Alexandria University, 7Zagazig University, 8South Valley University
Objective:
Evaluating the efficacy of 2D and 3D ResNet18-based convolutional neural network models in classifying Parkinson’s Disease patients from healthy controls using T1-weighted MRI images.
Background:
Parkinson’s Disease (PD) is a neurodegenerative disorder affecting millions worldwide, with progressive motor and non-motor symptoms. Early diagnosis is critical for optimal management, but current neuroimaging techniques can be complex and time-consuming. Recently, deep learning techniques and advancements have shown potential in automating diagnostic processes. This study aimed to assess the performance of 2D and 3D ResNet18-based convolutional neural network (CNN) models in distinguishing PD patients from healthy controls using T1-weighted magnetic resonance imaging (MRI).
Design/Methods:
We developed two CNN models: a 2D model that utilized mid-sagittal T1-weighted MRI slices, and a 3D model based on volumetric brain data. Preprocessing included data augmentation and transfer learning to enhance model performance. Data were split into 85% for training and 15% for testing, with performance evaluated through accuracy, sensitivity, specificity, and area under the curve (AUC). The models were trained and validated using GPU acceleration for optimized computational efficiency.
Results:
The 3D CNN model achieved an accuracy of 94%, outperforming the 2D CNN model, which had an accuracy of 91%. The 3D model also exhibited superior sensitivity (92% vs. 89%) and AUC (0.94 vs. 0.92). Confusion matrices revealed higher specificity and reduced false positives for the 3D model, highlighting its superior diagnostic performance.
Conclusions:
Our results demonstrate that the 3D ResNet18-based CNN significantly outperforms its 2D counterpart in classifying PD patients from T1-weighted MRI images, achieving higher accuracy, sensitivity, and AUC. The superior performance of the 3D model can be attributed to its ability to capture more complex anatomical features, enhancing its diagnostic capability. Further studies should aim to validate the findings across larger, more diverse populations and explore hybrid models that integrate 2D and 3D approaches. 
10.1212/WNL.0000000000212057
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