Research Article
Hearing loss classification via stationary wavelet entropy and Biogeography-based optimization
@ARTICLE{10.4108/eai.7-8-2020.165964, author={Chong Yao and Chaosheng Tan and Junding Sun}, title={Hearing loss classification via stationary wavelet entropy and Biogeography-based optimization}, journal={EAI Endorsed Transactions on e-Learning}, volume={6}, number={19}, publisher={EAI}, journal_a={EL}, year={2020}, month={8}, keywords={Virtual Private Network, designing secure enterprise network, secure enterprise network}, doi={10.4108/eai.7-8-2020.165964} }- Chong Yao
Chaosheng Tan
Junding Sun
Year: 2020
Hearing loss classification via stationary wavelet entropy and Biogeography-based optimization
EL
EAI
DOI: 10.4108/eai.7-8-2020.165964
Abstract
Introduction: Sensorineural hearing loss is associated with many complications and needs timely detection and diagnosis.
Objectives: Optimize the sensorineural hearing loss detection system to improve the accuracies of image detection.
Method: The stationary wavelet entropy was used to extract the features of NMR images, the single hidden layer neural network was used for classification, and the BBO algorithm was used for optimization to avoid the dilemma of local optimum. We used two-level SWE as input to the classifier to enhance the identify and classify ability of hearing loss.
Results: The results of 10-fold cross validation show that the accuracies of HC, LHL and RHL are 91.83± 3.09%, 92.67±2.38% and 91.17±2.61%, respectively. The overall accuracy is 91.89±0.70%.
Conclusion: This model has good performance in detecting hearing loss.
Copyright © 2020 Chong Yaoet al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.