COVID-19 Diagnosis by Wavelet Entropy and Particle Swarm Optimization

Wang, Jiaji

doi:10.1007/978-3-031-13829-4_52

Cited by 2 publications

(1 citation statement)

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“…Step 1: Wavelet Transform The first step involves applying the wavelet transform to the time series data [31]. This transform breaks down the signal into different scales, allowing for the analysis of both low and high-frequency components.…”

Section: Wavelet Entropymentioning

confidence: 99%

Multiple Sclerosis Recognition via Wavelet Entropy and PSO-Based Neural Network

Lima

2023

Preprint

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This paper presents a novel approach for the recognition of multiple sclerosis (MS) using wavelet entropy and a Particle Swarm Optimization (PSO)-based neural network. MS is a complex neurological disorder with diverse clinical manifestations, often challenging to diagnose accurately. In this study, we leverage wavelet entropy (WE) as a feature extraction method to capture intricate patterns within brain imaging data. These extracted features are then employed to train a neural network model optimized through PSO to enhance classification accuracy. Experimental results on a dataset comprising MS and healthy control subjects demonstrate the effectiveness of our proposed approach, viz., WE-PSONN, which achieves a sensitivity of 91.95±1.15, a specificity of 92.36±0.88, a precision of 92.28±0.88, an accuracy of 92.16±0.90. The combination of advanced signal processing techniques and machine learning optimization holds promise for improving the early diagnosis and management of MS, offering potential benefits to both patients and healthcare providers.

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Section: Wavelet Entropymentioning

confidence: 99%