Abstract
Hearing loss, a partial or total inability to hear, is known as hearing impairment. Untreated hearing loss can have a bad effect on normal social communication, and it can cause psychological problems in patients. Therefore, we design a three-category classification system to detect the specific category of hearing loss, which is beneficial to be treated in time for patients. Before the training and test stages, we use the technology of data augmentation to produce a balanced dataset. Then we use deep autoencoder neural network to classify the magnetic resonance brain images. In the stage of deep autoencoder, we use stacked sparse autoencoder to generate visual features, and softmax layer to classify the different brain images into three categories of hearing loss. Our method can obtain good experimental results. The overall accuracy of our method is 99.5%, and the time consuming is 0.078 s per brain image. Our proposed method based on stacked sparse autoencoder works well in classification of hearing loss images. The overall accuracy of our method is 4% higher than the best of state-of-the-art approaches.
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03 January 2018
The original version of this article unfortunately contained a mistake. The reference #27 in the reference list is incorrect in that the individual chapter should be cited instead of the whole book.
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Acknowledgements
This paper is supported by Natural Science Foundation of China (61602250), Natural Science Foundation of Jiangsu Province (BK20150983, BK20150982).
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional review board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
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This article is part of the Topical Collection on Image & Signal Processing.
A correction to this article is available online at https://doi.org/10.1007/s10916-017-0884-3.
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Jia, W., Yang, M. & Wang, SH. Three-Category Classification of Magnetic Resonance Hearing Loss Images Based on Deep Autoencoder. J Med Syst 41, 165 (2017). https://doi.org/10.1007/s10916-017-0814-4
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DOI: https://doi.org/10.1007/s10916-017-0814-4