Hearing loss classification via AlexNet and Support Vector Machine





AlexNet, Support Vector Machine, Hearing loss


This paper presents a new method for detecting hearing loss. Our approach is first to use AlexNet to extract the features. Then, we use the Support Vector Machine as a classifier to classify the images. 10-fold cross-validation results showed that the sensitivities of the healthy control group, the left-sided hearing loss group, and the right-sided hearing loss group in this method were 94.67%, 94.00%, and 95.17%, respectively, achieving a very good effect compared with other hearing loss detection methods. In conclusion, our method is effective for the identification of hearing loss.


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Wang, S., Detection of Left-Sided and Right-Sided Hearing Loss via Fractional Fourier Transform. Entropy, 2016. 18(5): Article ID. 194

Jeong, J., Neutrophil-to-lymphocyte ratio as a prognostic inflammatory factor in sudden sensorineural hearing loss. European Journal of Inflammation, 2022. 20: Article ID. 1721727x221144452

Muus, J.S., et al., Hearing loss in children with growth hormone deficiency. International journal of pediatric otorhinolaryngology, 2017. 100: p. 107

Wang, S., et al., Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning. Frontiers in Computational Neuroscience, 2016. 10: Article ID. 160

Jaradeh, K., et al., Hearing Loss Screening, Diagnosis, and Treatment for Refugees and Asylees in an Urban Clinic, 2014-2017. Oto Open, 2022. 6(4): Article ID. 2473974x221132509

Wang, S., Texture Analysis Method Based on Fractional Fourier Entropy and Fitness-scaling Adaptive Genetic Algorithm for Detecting Left-sided and Right-sided Sensorineural Hearing Loss. Fundamenta Informaticae, 2017. 151(1-4): p. 505-521

Loughrey, D.G., Is age-related hearing loss a potentially modifiable risk factor for dementia? Lancet Healthy Longevity, 2022. 3(12): p. E805-E806

Tripathi, P., et al., Sudden Sensorineural Hearing Loss: A Review. Cureus, 2022. 14(9): p. e29458

Zhou, T., et al., GAN review: Models and medical image fusion applications. Information Fusion, 2023. 91: p. 134-148

van der Velden, B.H.M., et al., Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal, 2022. 79: p. 102470

Alzubaidi, L., et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data, 2021. 8(1): p. 53

Krizhevsky, A., et al., ImageNet classification with deep convolutional neural networks. Communications of the ACM, 2017. 60(6): p. 84-90

Tang, L., et al. Hu Moment Invariant: A New Method for Hearing Loss Detection. in International Conference Advanced Engineering & Technology Research. 2018.

Gao, R., et al. Hearing loss identification by wavelet entropy and cat swarm optimization. in ADVANCES IN MATERIALS, MACHINERY, ELECTRONICS III: 3rd International Conference on Advances in Materials, Machinery, Electronics (AMME 2019). 2019.

Wang, L., et al. Hearing Loss Identification via Fractional Fourier Entropy and Direct Acyclic Graph Support Vector Machine. in International Conference on Multimedia Technology and Enhanced Learning. 2020.

Yao, X., et al. Hearing loss classification via stationary wavelet entropy and genetic algorithm. in 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC). 2020.

Yao, C., Hearing loss classification via stationary wavelet entropy and cat swarm optimization. Cognitive Systems and Signal Processing in Image Processing, 2022: p. 203-221

Zhang, Y., Detection of unilateral hearing loss by Stationary Wavelet Entropy. CNS & Neurological Disorders - Drug Targets, 2017. 16(2): p. 15-24

Zainal, A.G., et al., Recognition of Copy Move Forgeries in Digital Images using Hybrid Optimization and Convolutional Neural Network Algorithm. International Journal of Advanced Computer Science and Applications, 2022. 13(12): p. 301-311

Le Gratiet, B., et al., Deployment of convolutional neural network solutions for image computing in semiconductor manufacturing environment. Journal of Micro-Nanopatterning Materials and Metrology-Jm3, 2022. 21(4)

Alsharabi, N., et al., Implementing Magnetic Resonance Imaging Brain Disorder Classification via AlexNet-Quantum Learning. Mathematics, 2023. 11(2): Article ID. 376

Guo, Y., et al., Radar Moving Target Detection Method Based on SET2 and AlexNet. Mathematical Problems in Engineering, 2022. 2022: Article ID. 3359871

Zhang, Y.-D., High performance multiple sclerosis classification by data augmentation and AlexNet transfer learning model. Journal of Medical Imaging and Health Informatics, 2019. 9(9): p. 2012-2021

Sethy, P.K., et al., Lung cancer histopathological image classification using wavelets and AlexNet. Journal of X-Ray Science and Technology, 2023. 31(1): p. 211-221

Yee, N.L., et al., Apex Frame Spotting Using Attention Networks for Micro-Expression Recognition System. Cmc-Computers Materials & Continua, 2022. 73(3): p. 5331-5348

Kuo, C.F.J., et al., Automatic detection, classification and localization of defects in large photovoltaic plants using unmanned aerial vehicles (UAV) based infrared (IR) and RGB imaging. Energy Conversion and Management, 2023. 276: Article ID. 116495

Tripathi, S., et al., Denoising of magnetic resonance images using discriminative learning-based deep convolutional neural network. Technology and Health Care, 2022. 30(1): p. 145-160

Halle, S.D., et al., Bayesian dropout approximation in deep learning neural networks: analysis of self-aligned quadruple patterning. Journal of Micro-Nanopatterning Materials and Metrology-Jm3, 2022. 21(4): Article ID. 041604

Zhang, Y.-D., Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU. Journal of Computational Science, 2018. 28: p. 1-10

Saha, R.K., et al., Automated quantification of meibomian gland dropout in infrared meibography using deep learning. Ocular Surface, 2022. 26: p. 283-294

Gharekhani, M., et al., Quantifying the groundwater total contamination risk using an inclusive multi-level modelling strategy. Journal of Environmental Management, 2023. 332: Article ID. 117287

Fouad, I.A., A robust and efficient EEG-based drowsiness detection system using different machine learning algorithms. Ain Shams Engineering Journal, 2023. 14(3): Article ID. 101895

Behera, J., et al., Prediction based mean-value-at-risk portfolio optimization using machine learning regression algorithms for multi-national stock markets. Engineering Applications of Artificial Intelligence, 2023. 120: Article ID. 105843

Yan, Y., et al., Alcoholism via wavelet energy entropy and support vector machine, in Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion. 2022, Association for Computing Machinery: Leicester, United Kingdom. p. Article 3.




How to Cite

J. Wang, “Hearing loss classification via AlexNet and Support Vector Machine”, EAI Endorsed Trans AI Robotics, vol. 2, no. 1, p. e2, Apr. 2023.