Research Article
Hearing loss classification via AlexNet and Support Vector Machine
@ARTICLE{10.4108/airo.v2i1.3113, author={Jing Wang}, title={Hearing loss classification via AlexNet and Support Vector Machine}, journal={EAI Endorsed Transactions on AI and Robotics}, volume={2}, number={1}, publisher={EAI}, journal_a={AIRO}, year={2023}, month={4}, keywords={AlexNet, Support Vector Machine, Hearing loss}, doi={10.4108/airo.v2i1.3113} }- Jing Wang
Year: 2023
Hearing loss classification via AlexNet and Support Vector Machine
AIRO
EAI
DOI: 10.4108/airo.v2i1.3113
Abstract
This paper presents a new method for detecting hearing loss. Our approach is first to use AlexNet to extract the features. Then, we use the Support Vector Machine as a classifier to classify the images. 10-fold cross-validation results showed that the sensitivities of the healthy control group, the left-sided hearing loss group, and the right-sided hearing loss group in this method were 94.67%, 94.00%, and 95.17%, respectively, achieving a very good effect compared with other hearing loss detection methods. In conclusion, our method is effective for the identification of hearing loss.
Copyright © 2023 Jing Wang et 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.
