airo 23(1): e2

Research Article

Hearing loss classification via AlexNet and Support Vector Machine

Download104 downloads
Cite
BibTeX Plain Text
  • @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
Jing Wang1,*
  • 1: Henan Polytechnic University
*Contact email: wangjing@home.hpu.edu.cn

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.

Keywords
AlexNet, Support Vector Machine, Hearing loss
Received
2023-03-07
Accepted
2023-03-21
Published
2023-04-21
Publisher
EAI
http://dx.doi.org/10.4108/airo.v2i1.3113

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.