Work

Parkinson’s Disease Classification Using Gray Matter MRI and Deep Learning: A Comparative Framework

Year: 2025

Type: article

Abstract: In this study, we propose multiple deep learning models for classifying gray matter MRI images of healthy individuals, prodromal Parkinson’s disease (PD) subjects, and diagnosed PD patients. The two proposed models extend conventional deep learning architectures—MedicalNet3D and 3D ResNet18—by performing feature extraction separately for each class and inputting these features into distinct multilayer perceptron (MLP) classifiers constructed via fine-tuning. To mitigate overfitting problem and improve generalizability, we introduce a training method based on group-wise feature fusion, in which subject IDs are separated to avoid data leakage during training. Through comparative experiments using the PPMI database, the effectiveness of the proposed approach was validated.

Source: Applied Sciences

DOI: 10.3390/app152111812

Authors: Haotian Li, Tong Liang, Runhong Yao, Takashi Kuremoto

Institutions: Nippon Institute of Technology, Juntendo University, Nihon Institute of Medical Science

Language: English

Topic: Parkinson's Disease Mechanisms and Treatments

Subfield: Neurology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,896