Hearing loss classification via stationary wavelet entropy and Biogeography-based optimization





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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.




How to Cite

C. Yao, C. Tan, and J. Sun, “Hearing loss classification via stationary wavelet entropy and Biogeography-based optimization”, EAI Endorsed Trans e-Learn, vol. 6, no. 19, p. e5, Aug. 2020.