Classifying Parkinson’s Disease Patients from Healthy Controls Using a ResNet18 Convolutional Neural Network Model of T1-Weighted MRI (P9-5.020)
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.
Authors
- Amr Hassan (ORCID: https://orcid.org/0000-0001-7374-7074)
- Mohamed Diab
- Ahmed Negida (ORCID: https://orcid.org/0000-0001-5363-6369)
- Mahmoud Tarek Hefnawy (ORCID: https://orcid.org/0000-0002-7912-6399)
- Brian D. Berman (ORCID: https://orcid.org/0000-0002-0602-9942)
- Matthew J. Barrett (ORCID: https://orcid.org/0000-0003-4480-0221)
- Rehab Adel Diab (ORCID: https://orcid.org/0000-0002-6552-4900)
- Ahmed Y. Azzam (ORCID: https://orcid.org/0000-0002-4256-0159)
- Ibrahim Serag (ORCID: https://orcid.org/0000-0002-0085-022X)
- Mohammed Ahmed Ali
Institutions
- Mansoura University (EG)
- Virginia Commonwealth University (US)
- South Valley University (EG)
- Zagazig University (EG)
- University of California System (US)
- Al-Azhar University (ID)
- October 6 University (EG)
- Alexandria University (EG)
Publication Details
- Journal
- Neurology
- Published
- 2025-04-07
- DOI
- https://doi.org/10.1212/wnl.0000000000212057
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00