Analyzing cross-validation in compressed sensing with Poisson noise

Rajasekaran, Sudarsanan; Rajwade, Ajit

doi:10.1016/j.sigpro.2020.107947

Cited by 6 publications

(1 citation statement)

References 21 publications

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“…Cross-validation is one of the practical solutions to such problems, with its ability to improve data usage by repeating the disruption and partitioning steps. In this study, a 10-fold cross-validation [23] is used.…”

Section: K-fold Cross-validationmentioning

confidence: 99%

Covid-19 Detection by Wavelet Entropy and Self-adaptive PSO

Wang

Górriz

et al. 2022

Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications

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The rapid global spread of COVID-19 disease poses a huge threat to human health and the global economy. The rapid increase in the number of patients diagnosed has strained already scarce healthcare resources to track and treat Covid-19 patients in a timely and effective manner.The search for a fast and accurate way to diagnose Covid-19 has attracted the attention of many researchers. In our study, a deep learning framework for the Covid-19 diagnosis task was constructed using wavelet entropy as a feature extraction method and a feedforward neural network classifier, which was trained using an adaptive particle swarm algorithm. The model achieved an average sensitivity of 85.14% ± 2.74%, specificity of 86.76% ± 1.75%, precision of 86.57% ± 1.36%, accuracy of 85.95% ± 1.14%, and F1 score of 85.82% ± 1.30%, Matthews correlation coefficient of 71.95 ± 2.26%, and Fowlkes-Mallows Index of 85.83% ± 1.30%. Our experiments validate the usability of wavelet entropy-based feature extraction methods in the medical image domain and show the non-negligible impact of different optimisation algorithms on the models by comparing them with other models.

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Section: K-fold Cross-validationmentioning

confidence: 99%