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

Farhan Mohammed, Xiangjian He, Yiguang Lin

Research output: Journal PublicationArticlepeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Article number101810
JournalComputerized Medical Imaging and Graphics
Volume87
DOIs
Publication statusPublished - Jan 2021
Externally publishedYes

Keywords

  • CNNs
  • Deep learning
  • Image classification
  • Parkinson's disease
  • SPECT

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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