Parkinson’s disease diagnosis using neural networks: Survey and comprehensive evaluation
作者：
Highlights：
• We provide the most relevant information collected from 143 papers published from 2013–2021 on diagnosis and classification of Parkinson’s disease.
• We used artificial and deep neural networks in a highly compact manner within this paper.
• We design this paper in a manner that enables a reader to objectively compare the network architectures used by the researchers.
• We provide insights on various aspects of deep networks and their training configurations used by researchers and discuss their efficacy.
• We provide numerous future directions by the help of our discussions and supporting materials.
摘要
•We provide the most relevant information collected from 143 papers published from 2013–2021 on diagnosis and classification of Parkinson’s disease.•We used artificial and deep neural networks in a highly compact manner within this paper.•We design this paper in a manner that enables a reader to objectively compare the network architectures used by the researchers.•We provide insights on various aspects of deep networks and their training configurations used by researchers and discuss their efficacy.•We provide numerous future directions by the help of our discussions and supporting materials.
论文关键词：Parkinson’s disease,Deep learning,Neural networks,Multi-modal learning,Machine Learning,Diagnosis
论文评审过程：Received 16 August 2021, Revised 1 February 2022, Accepted 14 February 2022, Available online 21 March 2022, Version of Record 21 March 2022.
论文官网地址：https://doi.org/10.1016/j.ipm.2022.102909
