ELHnet: a convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography
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| Authors | George S. Liu, Michael H. Zhu, Jinkyung Kim, Patrick Raphael, Brian E. Applegate, John S. Oghalai |
| Journal/Conference Name | Biomedical Optics Express |
| Paper Category | and Optics, Atomic and Molecular Physics |
| Paper Abstract | Detection of endolymphatic hydrops is important for diagnosing Meniere's disease, and can be performed non-invasively using optical coherence tomography (OCT) in animal models as well as potentially in the clinic. Here, we developed ELHnet, a convolutional neural network to classify endolymphatic hydrops in a mouse model using learned features from OCT images of mice cochleae. We trained ELHnet on 2159 training and validation images from 17 mice, using only the image pixels and observer-determined labels of endolymphatic hydrops as the inputs. We tested ELHnet on 37 images from 37 mice that were previously not used, and found that the neural network correctly classified 34 of the 37 mice. This demonstrates an improvement in performance from previous work on computer-aided classification of endolymphatic hydrops. To the best of our knowledge, this is the first deep CNN designed for endolymphatic hydrops classification. |
| Date of publication | 2017 |
| Code Programming Language | Python |
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