A deep learning approach for classification of Parkinson’s disease using single photon emission computerized tomography
摘要
In this article, a novel deep neural network architecture is presented for distinguishing subjects of Parkinson’s Disease (PD) from Cognitively Normal (CN) cohort. The proposed architecture combines the representation learning capacities and discriminating capabilities of convolutional neural network to improve classification performance of the network. For this purpose, the network consists of a classification sub-network and multiple reconstruction sub-networks. The classification sub-network discriminates between PD and CN subjects and the reconstruction sub-networks are used to regularize the individual layers of the classification sub-network. All sub-networks are trained simultaneously to optimize the customized objective function of the network. The network is trained on Single Photon Emission Computerized Tomography (SPECT) images obtained from baseline study of Parkinson’s Progression Marker Initiative (PPMI). The proposed approach provides classification accuracy, recall, precision and F1-score of