An easy-to-use deep-learning model for highly accurate diagnosis of Parkinson's disease using SPECT images. (January 2021)

• Record Type: Journal Article
• Authors: Mohammed, Farhan; He, Xiangjian; Lin, Yiguang
• Is Part Of: Computerized medical imaging and graphics. Volume 87(2021)
• Publication Date: 2021-01
• Subjects: Deep learning -- CNNs -- Image classification -- Parkinson's disease -- SPECT; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2020.101810
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: Developed a CNN-based network model for the Parkinson's disease (PD) diagnosis. A total of 2723 SPECT images (biggest PD sample size) were analyzed in the study Achieved the highest-level performance in accuracy, sensitivity & specificity The deep-learning network model is fully automated and is simple-to-use The model has the potential to revolutionize the diagnosis of PD in the future Abstract: Accurate diagnosis of Parkinson's Disease (PD) at its early stages remains a challenge for modern clinicians. In this study, we utilize a convolutional neural network (CNN) approach to address this problem. In particular, we develop a CNN-based network model highly capable of discriminating PD patients based on Single Photon Emission Computed Tomography (SPECT) images from healthy controls. A total of 2723 SPECT images are analyzed in this study, of which 1364 images from the healthy control group, and the other 1359 images are in the PD group. Image normalization process is carried out to enhance the regions of interests (ROIs) necessary for our network to learn distinguishing features from them. A 10-fold cross-validation is implemented to evaluate the performance of the network model. Our approach demonstrates outstanding performance with an accuracy of 99.34 %, sensitivity of 99.04 % and specificity of 99.63 %, outperforming all previously published results. Given the high performance and easy-to-use features of our network, it can be deduced that our approach has the potential to revolutionize the diagnosis of PD and its management.