FF-MSPAM: A Multi-scale Parallel Attention Mechanism based Feature Fusion Model for Diagnosis of Parkinson's Disease

Parkinson's disease (PD) is a chronic neurodegenerative disease ranked second in the world. PD is often diagnosed using brain magnetic resonance imaging (MRI), which is a promising technique for PD biomarker development. However, it is difficult to focus on the pathogenic areas in the brain MRI of PD. Therefore, accurately capturing the characteristics of pathogenic areas has become an important issue. We propose a novel computational model (FF-MSPAM) for PD diagnosis by learning T2 weighted 3D-MRI slice features. First, in order to reduce parameters and accelerate training speed, a mixed network with ordinary convolution and separable convolution (OS-CNN) is designed. Next, VGG19 is applied for feature fusion to extract richer features. Finally, a multi-scale parallel attention mechanism (MSP-AM) was established to focus and aggregate spatial and channel features at different scales. The applicability of the proposed model was demonstrated using T2 weighted 3D-MRI slices of 168 subjects obtained from publicly available database. We have achieved classification accuracy of up to 98% in the differential diagnosis of PD. The experiment shows that our method is successful. Good results were obtained in PD diagnosis task and compared with advanced research models. Our model can be used for the diagnosis and prognosis of PD.