An optimized framework for Parkinson's disease classification using multimodal neuroimaging data with ensemble-based and data fusion networks.

Abstract

Parkinson's disease (PD) is a neurodegenerative disease that affects both the motor and nonmotor functions of an individual and is more prevalent in older adults. PD is preceded by an early stage called prodromal PD, which starts very early before the typical symptoms of the disease appear. If patients are managed and diagnosed at this initial stage, their quality of life can be maintained. Magnetic Resonance Imaging (MRI) is a widespread approach in neuroimaging that is very helpful in the diagnosis of brain-related diseases. Current studies of PD classification mostly use T1-weighted MRI or other modalities. T2-FLAIR MRI, including the multimodal techniques that employ it, is understudied despite its ability to reliably identify white matter lesions in the brain, which directly aids in diagnosing PD. In this study, two networks based on deep learning and machine learning are proposed for better and early disease classification using multimodal data, including the T1-weighted, T2-FLAIR MRI, and Montreal Cognitive Assessment (MoCA) score datasets. The datasets were downloaded from an online longitudinal study called the Parkinson's Progression Markers Initiative (PPMI). The first network is an ensemble-based network that combines three deep learning models, MobileNet, EfficientNet, and a custom Convolutional Neural Network (CNN), and the second network blends a custom CNN trained on both MRI modalities and a multilayer perceptron (MLP) trained on the MoCA score dataset followed by an attention module, thus providing a multimodal fusion network. Both networks achieve efficient results with respect to different evaluation metrics. The ensemble model attained an accuracy of 97.1 %, a sensitivity of 96.2 %, a precision of 96.4 %, an F1 score of 96.3 %, and a specificity of 97.4 %, while the data fusion model achieved an accuracy of 97.9 %, a sensitivity of 97.1 %, a precision of 97.6 %, an F1 score of 97.3 %, and a specificity of 98 %. Grad-CAM analysis was employed to visualize the key brain regions contributing to model decisions, thereby enhancing transparency and clinical relevance.