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Article summary:
1. This article presents a novel cascaded multi-column RVFL+ (cmcRVFL+) framework for single-modal neuroimaging-based diagnosis of Parkinson's Disease (PD).
2. The cmcRVFL+ algorithm uses the predicted values of RVFL+ classifiers in the current layers as privileged information for the following classifiers, and only the multi-column RVFL+ classifiers in the last layer are ensembled to generate the predictive result.
3. Experimental results on both transcranial sonography and magnetic resonance imaging data sets show that cmcRVFL+ achieves superior performance to all compared algorithms, suggesting its potential application to various single-modal imaging based CADs.
Article analysis:
This article provides a detailed description of a novel cascaded multi-column RVFL+ (cmcRVFL+) framework for single-modal neuroimaging-based diagnosis of Parkinson's Disease (PD). The authors present their findings from experiments conducted on both transcranial sonography and magnetic resonance imaging data sets, which demonstrate that cmcRVFL+ achieves superior performance to all compared algorithms.
The article is generally reliable and trustworthy, as it provides detailed descriptions of the methods used and presents evidence from experiments conducted on two different data sets. However, there are some potential biases that should be noted. For example, the authors do not discuss any possible risks associated with using this method for PD diagnosis or explore any counterarguments to their findings. Additionally, they do not provide any evidence for their claims about cmcRVFL+'s potential application to various single-modal imaging based CADs. Furthermore, they do not present both sides equally; instead they focus solely on presenting their own findings without considering other perspectives or approaches.
In conclusion, while this article is generally reliable and trustworthy due to its detailed descriptions of methods used and evidence presented from experiments conducted on two different data sets, there are some potential biases that should be noted such as lack of discussion about possible risks associated with using this method for PD diagnosis or exploration of counterarguments to their findings, lack of evidence for claims about cmcRVFL+'s potential application to various single-modal imaging based CADs, and lack of presentation of both sides equally.