@inproceedings{a93a2913697b40a182d4b975ca31686d,
title = "Various Patient classification model using ABR data",
abstract = "Auditory brain response (ABR) is an auditory test developed in the 1970s that enables objective testing regardless of age. However, ABR has the disadvantage that analysis and reading are essential because it is complicated to inspect and requires trained inspectors. In this paper, we present a deep learning Keras sequential model and a VGG16 model that distinguish between deaf patients and normal people using ABR data and compare the performance and accuracy of the model. It is expected that a deep learning model that automatically distinguishes deaf patients from normal people will be applied in the future using the proposed model.",
keywords = "ABR, Deep learning, Sequential model, VGG16 model",
author = "Jun Ma and Choi, \{Seong Jun\} and Jaehyoun Kim and Min Hong",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022 ; Conference date: 14-12-2022 Through 16-12-2022",
year = "2022",
doi = "10.1109/CSCI58124.2022.00037",
language = "English",
series = "Proceedings - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "189--194",
booktitle = "Proceedings - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022",
}