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Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

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Performance Comparison of Convolutional Neural Network-Based Hearing Loss Classification Model Using Auditory

Jun Ma¹, Seong Jun Choi², Sungyeup Kim³

¹Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.

Diagnostics (Basel, Switzerland)

|June 27, 2024

Summary

This summary is machine-generated.

This study shows AlexNet, a deep learning model, can accurately classify hearing loss from auditory brainstem response (ABR) images with 95.93% accuracy, aiding in automated diagnosis.

Keywords:

ABR Alexnet DenseNet121 Densenet201 VGG16 VGG19 auditory brainstem response deep learning hearing loss image processing

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Area of Science:

Medical Imaging
Artificial Intelligence
Audiology

Background:

Hearing loss diagnosis relies on auditory brainstem response (ABR) data.
Automating ABR analysis can improve diagnostic efficiency.

Purpose of the Study:

To evaluate Convolutional Neural Network (CNN) models for hearing loss classification using ABR images.
To compare the performance of six CNN architectures: VGG16, VGG19, DenseNet121, DenseNet-201, AlexNet, and InceptionV3.

Main Methods:

Utilized a dataset of 7990 preprocessed ABR images.
Systematically tested six CNN models for classification accuracy.
Performed comparative analysis based on accuracy and computational efficiency.

Main Results:

AlexNet achieved the highest accuracy at 95.93% in classifying hearing loss.
Demonstrated the effectiveness of deep learning models in ABR image analysis.

Conclusions:

Deep learning, specifically AlexNet, shows significant potential for automated hearing loss diagnosis from ABR graphs.
Further refinement of models can enhance clinical application and diagnostic accuracy.