Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response

• URL: http://arxiv.org/abs/2308.16203v1
• Date: Tue, 29 Aug 2023 10:40:12 GMT
• Title: Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response
• Authors: Fatih Ozyurt, Jafar Majidpour, Tarik A. Rashid, Amir Majidpour, Canan Koc
• Abstract summary: Inaccurate assessment of auditory brainstem response (ABR) tests may lead to incorrect judgments regarding the integrity of the auditory nerve system. This study proposed deep-learning models using the transfer-learning (TL) approach to extract features from ABR testing and diagnose Hearing Loss using support vector machines (SVM) It has been decided to use six measures accuracy, precision, recall, geometric mean (GM), standard deviation (SD), and area under the ROC curve to measure the effectiveness of the proposed model.
• Score: 4.023511716339818
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The assessment of the well-being of the peripheral auditory nerve system in individuals experiencing hearing impairment is conducted through auditory brainstem response (ABR) testing. Audiologists assess and document the results of the ABR test. They interpret the findings and assign labels to them using reference-based markers like peak latency, waveform morphology, amplitude, and other relevant factors. Inaccurate assessment of ABR tests may lead to incorrect judgments regarding the integrity of the auditory nerve system; therefore, proper Hearing Loss (HL) diagnosis and analysis are essential. To identify and assess ABR automation while decreasing the possibility of human error, machine learning methods, notably deep learning, may be an appropriate option. To address these issues, this study proposed deep-learning models using the transfer-learning (TL) approach to extract features from ABR testing and diagnose HL using support vector machines (SVM). Pre-trained convolutional neural network (CNN) architectures like AlexNet, DenseNet, GoogleNet, InceptionResNetV2, InceptionV3, MobileNetV2, NASNetMobile, ResNet18, ResNet50, ResNet101, ShuffleNet, and SqueezeNet are used to extract features from the collected ABR reported images dataset in the proposed model. It has been decided to use six measures accuracy, precision, recall, geometric mean (GM), standard deviation (SD), and area under the ROC curve to measure the effectiveness of the proposed model. According to experimental findings, the ShuffleNet and ResNet50 models' TL is effective for ABR to diagnose HL using an SVM classifier, with a high accuracy rate of 95% when using the 5-fold cross-validation method.

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