Trusted detection for Parkinson’s disease based on uncertainty estimation

doi:10.19682/j.cnki.1005-8885.2024.0019

The Journal of China Universities of Posts and Telecommunications ›› 2024, Vol. 31 ›› Issue (5): 85-94.doi: 10.19682/j.cnki.1005-8885.2024.0019

Received:2023-09-11 Revised:2024-03-31 Online:2024-10-31 Published:2024-10-31
Contact: Yun LI E-mail:liyun@njupt.edu.cn

Abstract

Abstract: Currently, most deep learning methods used for Parkinson’s disease ( PD) detection lack reliability assessment. This characteristic makes it is difficult to identify erroneous results in practice, leading to potentially serious consequences. To address this issue, a prior network with the distance measure ( PNDM) layer was proposed in this paper. PNDM layer consists of two modules: prior network ( PN) and the distance measure ( DM) layer. The prior network is employed to estimate data uncertainty, and the DM layer is utilized to estimate model uncertainty. The goal of this work is to provide accurate and reliable PD detection through uncertainty estimation. Experiments show that PNDM layer can effectively estimate both model uncertainty and data uncertainty, rendering it more suitable for uncertainty estimation in PD detection compared to existing methods.

Key words:

Parkinson’s disease ( PD), deep learning, uncertainty estimation

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