Version 1
: Received: 9 October 2023 / Approved: 10 October 2023 / Online: 10 October 2023 (11:53:13 CEST)
How to cite:
Lima, D. Multiple Sclerosis Recognition via Wavelet Entropy and PSO-Based Neural Network. Preprints2023, 2023100642. https://doi.org/10.20944/preprints202310.0642.v1
Lima, D. Multiple Sclerosis Recognition via Wavelet Entropy and PSO-Based Neural Network. Preprints 2023, 2023100642. https://doi.org/10.20944/preprints202310.0642.v1
Lima, D. Multiple Sclerosis Recognition via Wavelet Entropy and PSO-Based Neural Network. Preprints2023, 2023100642. https://doi.org/10.20944/preprints202310.0642.v1
APA Style
Lima, D. (2023). Multiple Sclerosis Recognition via Wavelet Entropy and PSO-Based Neural Network. Preprints. https://doi.org/10.20944/preprints202310.0642.v1
Chicago/Turabian Style
Lima, D. 2023 "Multiple Sclerosis Recognition via Wavelet Entropy and PSO-Based Neural Network" Preprints. https://doi.org/10.20944/preprints202310.0642.v1
Abstract
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.
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
