Performance Comparison of Convolutional Neural Network-Based Hearing Loss Classification Model Using Auditory Brainstem Response Data Article Swipe
Jun Ma
,
Seong Jun Choi
,
Sungyeup Kim
,
Min Hong
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14121232
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14121232
This study evaluates the efficacy of several Convolutional Neural Network (CNN) models for the classification of hearing loss in patients using preprocessed auditory brainstem response (ABR) image data. Specifically, we employed six CNN architectures—VGG16, VGG19, DenseNet121, DenseNet-201, AlexNet, and InceptionV3—to differentiate between patients with hearing loss and those with normal hearing. A dataset comprising 7990 preprocessed ABR images was utilized to assess the performance and accuracy of these models. Each model was systematically tested to determine its capability to accurately classify hearing loss. A comparative analysis of the models focused on metrics of accuracy and computational efficiency. The results indicated that the AlexNet model exhibited superior performance, achieving an accuracy of 95.93%. The findings from this research suggest that deep learning models, particularly AlexNet in this instance, hold significant potential for automating the diagnosis of hearing loss using ABR graph data. Future work will aim to refine these models to enhance their diagnostic accuracy and efficiency, fostering their practical application in clinical settings.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics14121232
- https://www.mdpi.com/2075-4418/14/12/1232/pdf?version=1718177487
- OA Status
- gold
- Cited By
- 2
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399586840
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399586840Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/diagnostics14121232Digital Object Identifier
- Title
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Performance Comparison of Convolutional Neural Network-Based Hearing Loss Classification Model Using Auditory Brainstem Response DataWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-06-12Full publication date if available
- Authors
-
Jun Ma, Seong Jun Choi, Sungyeup Kim, Min HongList of authors in order
- Landing page
-
https://doi.org/10.3390/diagnostics14121232Publisher landing page
- PDF URL
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https://www.mdpi.com/2075-4418/14/12/1232/pdf?version=1718177487Direct link to full text PDF
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- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2075-4418/14/12/1232/pdf?version=1718177487Direct OA link when available
- Concepts
-
Computer science, Convolutional neural network, Auditory brainstem response, Hearing loss, Artificial intelligence, Speech recognition, Deep learning, Pattern recognition (psychology), Machine learning, Audiology, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | Diagnostics |
| primary_location.landing_page_url | https://doi.org/10.3390/diagnostics14121232 |
| publication_date | 2024-06-12 |
| publication_year | 2024 |
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| corresponding_author_ids | https://openalex.org/A5006130083 |
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| corresponding_institution_ids | https://openalex.org/I24541011 |
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