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

Han, Ji; Hou, Shouming

doi:10.1007/978-3-030-20518-8_8

Cited by 11 publications

(20 citation statements)

References 31 publications

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“… Yahia et al (2018) presented a new method—decimal descriptor pattern (DDP)—to assess MS lesions. Han and Hou (2019) combined wavelet entropy (WE) and adaptive genetic algorithm (AGA) to detect MS. The authors chose a three-level decomposition using the db2 wavelet.…”

Section: Introductionmentioning

confidence: 99%

“…This study proposes an improved algorithm based on Han and Hou (2019) instead of DL, as our dataset is small. The experiments show that our method is better than the two DL methods.…”

Section: Introductionmentioning

confidence: 99%

“…The experiments show that our method is better than the two DL methods. Four improvements are proposed based on Han and Hou (2019) . First, we use bior4.4 to replace the db2 wavelet in Han and Hou (2019) .…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

2021

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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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Section: Introductionmentioning

confidence: 99%

“…This study proposes an improved algorithm based on Han and Hou (2019) instead of DL, as our dataset is small. The experiments show that our method is better than the two DL methods.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

2021

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“…4 , the comparison results are more intuitive in the form of a histogram. From an precision point of view, the experimental results of HWT+PCA+LR [ 5 ] and WE-FNN-AGA [ 6 ] were 87.51% and 86.32 ± 0.86%, respectively. However, the precision of our method is 91.70 ± 0.78%, so it is significantly better than the two state-of-the-art approaches.…”

Section: Resultsmentioning

confidence: 99%

“…Lopez (2017) [ 5 ] proposed a hybrid method combining principal component (PCA), Haar wavelet transform (HWT), and logistic regression (LR). Han and Hou (2019) [ 6 ] proposed a wavelet entropy (WE) with feedforward neural network (FNN), whose weights and biases were optimized through a new swarm intelligence method—adaptive genetic algorithm (AGA).…”

Section: Introductionmentioning

confidence: 99%

A Multiple Sclerosis Recognition via Hu Moment Invariant and Artificial Neural Network Trained by Particle Swarm Optimization

Han

Hou

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Multiple sclerosis can damage the central nervous system, and current drugs are difficult to completely cure symptoms. The aim of this paper was to use deep learning methods to increase the detection rate of multiple sclerosis, thereby increasing the patient’s chance of treatment. We presented a new method based on hu moment invariant and artificial neural network trained by particle swarm optimization. Our method was carried out over ten runs of ten-fold cross validation. The experimental results show that the optimization ability of particle swarm optimization algorithm is superior to the genetic algorithm, simulated annealing algorithm and immune genetic algorithm. At the same time, compared with the HWT+PCA+LR method and the WE-FNN-AGA method, our method performs better in the performance of the detection.

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Diagnosis of secondary pulmonary tuberculosis by an eight-layer improved convolutional neural network with stochastic pooling and hyperparameter optimization

Zhang

Nayak

Zhang

et al. 2020

J Ambient Intell Human Comput

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

Cited by 11 publications

References 31 publications

RETRACTED: Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

RETRACTED: Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

A Multiple Sclerosis Recognition via Hu Moment Invariant and Artificial Neural Network Trained by Particle Swarm Optimization

Diagnosis of secondary pulmonary tuberculosis by an eight-layer improved convolutional neural network with stochastic pooling and hyperparameter optimization

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