Multiple Sclerosis Detection via Wavelet Entropy and Feedforward Neural Network Trained by Adaptive Genetic Algorithm

2019 ◽  
pp. 87-97 ◽  
Author(s):  
Ji Han ◽  
Shou-Ming Hou
Keyword(s):  
Neural Network ◽  
Multiple Sclerosis ◽  
Genetic Algorithm ◽  
Feedforward Neural Network ◽  
Adaptive Genetic Algorithm ◽  
Wavelet Entropy

scholarly journals Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

2021 ◽  
Vol 15 ◽  
Author(s):  
Shui-Hua Wang ◽  
Xianwei Jiang ◽  
Yu-Dong Zhang
Keyword(s):  
Multiple Sclerosis ◽  
Genetic Algorithm ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
Adaptive Genetic Algorithm ◽  
Biorthogonal Wavelet ◽  
Wide Range ◽  
Wavelet Features ◽  
The Brain ◽  
Better Than

Aim: Multiple sclerosis (MS) is a disease, which can affect the brain and/or spinal cord, leading to a wide range of potential symptoms. This method aims to propose a novel MS recognition method.Methods: First, the bior4.4 wavelet is used to extract multiscale coefficients. Second, three types of biorthogonal wavelet features are proposed and calculated. Third, fitness-scaled adaptive genetic algorithm (FAGA)—a combination of standard genetic algorithm, adaptive mechanism, and power-rank fitness scaling—is harnessed as the optimization algorithm. Fourth, multiple-way data augmentation is utilized on the training set under the setting of 10 runs of 10-fold cross-validation. Our method is abbreviated as BWF-FAGA.Results: Our method achieves a sensitivity of 98.00 ± 0.95%, a specificity of 97.78 ± 0.95%, and an accuracy of 97.89 ± 0.94%. The area under the curve of our method is 0.9876.Conclusion: The results show that the proposed BWF-FAGA method is better than 10 state-of-the-art MS recognition methods, including eight artificial intelligence-based methods, and two deep learning-based methods.

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