Title: A deep learning approach for the early diagnosis of Parkinson's disease using brain MRI scans
Authors: Rishik Mishra; Anand Singh Jalal; Manoj Kumar; Sunita Jalal
Addresses: GLA University, Mathura, UP 281406, India ' GLA University, Mathura, UP 281406, India ' GLA University, Mathura, UP 281406, India ' G.B. Pant University of Technology and Agriculture (GBPUAT), Udham Singh Nagar, Pantnagar, Uttarakhand 263153, India
Abstract: Parkinson's disease (PD) is a neurological disorder with more than six million people worldwide suffering from it. It is commonly diagnosed using clinical assessments and progression scale, which usually depends on the medical practitioner's expertise, and accuracy varies greatly between various examiners which also takes a long time to accurately diagnose. This paper proposes to develop a computer-aided diagnostic method to diagnose PD patients using MRI images of the brain, thus reducing cross-examiner variability and the time required to accurately differentiate between PD and control subjects. We have developed a CNN-based CAD system that classifies between PD and healthy patients, by utilising the differences between the Substantia Nigra region of the brain. The method proposed in this paper was successfully able to achieve an accuracy of 99.5%.
Keywords: computer-aided diagnosis; magnetic resonance imaging; MRI; deep learning; Parkinson's disease; PD; image processing; convolution neural networks; CNN.
International Journal of Applied Pattern Recognition, 2022 Vol.7 No.1, pp.64 - 77
Received: 04 Sep 2021
Accepted: 09 Jan 2022
Published online: 14 Apr 2022 *