Diagnosis of Parkinson's disease based on feature fusion on T2 MRI images

Deep-learning methods (especially convolutional neural networks) using magnetic resonance imaging (MRI) data have been successfully applied to computer-aided diagnosis of Parkinson's Disease (PD). Early detection and prior care may help patients improve their quality of life, although this neurodegenerative disease has no known cure. In this study, we propose a FResnet18 model to classify MRI images of PD and Health Control (HC) by fusing image texture features with deep features. First, Local Binary Pattern and Gray-Level Co-occurrence Matrix are used to extract the handcrafted features. Second, the modified ResNet18 network is used to extract deep features. Finally, the fused features are classified by Support Vector Machine. The classification accuracy rate for MRI images reaches 98.66%, and the findings demonstrate that the model can successfully differentiate between PD and HC. The suggested FResnet18 provides greater performance compared with existing approaches, and it is shown through extensive experimental findings on the Parkinson's Disease Progression Markers Initiative data set that feature fusion may improve classification performance.

Automated summary

In this study, a FResnet18 model is proposed to classify MRI images of PD and HC by fusing image texture features with deep features. The results show that the model can successfully differentiate between PD and HC with high accuracy, and it outperforms existing approaches.