Three-Category Classification of Magnetic Resonance Hearing Loss Images Based on Deep Autoencoder

Wenjuan Jia, Ming Yang, Shui Hua Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


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.

Original languageEnglish
Article number165
JournalJournal of Medical Systems
Issue number10
Publication statusPublished - 1 Oct 2017
Externally publishedYes


  • Data augmentation
  • Minibatch scaled conjugate gradient
  • Regularization
  • Stacked sparse autoencoder


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