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.

翻译：外周听觉神经系统在听力受损个体中的健康状况通过听觉脑干反应（ABR）检测进行评估。听力学家对ABR测试结果进行评定和记录。他们解读发现，并使用基于参考的标记（如波峰潜伏期、波形形态、振幅及其他相关因素）为其赋予标签。对ABR测试的不准确评估可能导致对听觉神经系统完整性的错误判断；因此，正确的听力损失（HL）诊断和分析至关重要。为了在降低人为错误可能性的同时识别和评估ABR自动化，机器学习方法（尤其是深度学习）可能是一个合适的选择。针对这些问题，本研究提出了采用迁移学习（TL）方法的深度学习模型，以从ABR测试中提取特征，并利用支持向量机（SVM）诊断HL。在所提出的模型中，使用预训练的卷积神经网络（CNN）架构（如AlexNet、DenseNet、GoogleNet、InceptionResNetV2、InceptionV3、MobileNetV2、NASNetMobile、ResNet18、ResNet50、ResNet101、ShuffleNet和SqueezeNet）从收集的ABR报告图像数据集中提取特征。决定采用六项指标（准确率、精确率、召回率、几何均值（GM）、标准差（SD）和ROC曲线下面积）来衡量所提出模型的有效性。实验结果表明，ShuffleNet和ResNet50模型的TL在使用SVM分类器诊断HL的ABR中效果显著，采用5折交叉验证方法时准确率高达95%。