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Cited 19 time in webofscience Cited 26 time in scopus
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Automatic detection of tympanic membrane and middle ear infection from oto-endoscopic images via convolutional neural networks

Authors
Khan, Mohammad AzamKwon, SoonwookChoo, JaegulHong, Seok MinKang, Sung HunPark, Il-HoKim, Sung KyunHong, Seok Jin
Issue Date
Jun-2020
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Otoscope; Tympanic membrane; Otitis media; Convolutional neural networks
Citation
Neural Networks, v.126, pp.384 - 394
Indexed
SCIE
SCOPUS
Journal Title
Neural Networks
Volume
126
Start Page
384
End Page
394
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/796
DOI
10.1016/j.neunet.2020.03.023
ISSN
0893-6080
Abstract
Convolutional neural networks (CNNs), a popular type of deep neural network, have been actively applied to image recognition, object detection, object localization, semantic segmentation, and object instance segmentation. Accordingly, the applicability of deep learning to the analysis of medical images has increased. This paper presents a novel application of state-of-the-art CNN models, such as DenseNet, to the automatic detection of the tympanic membrane (TM) and middle ear (ME) infection. We collected 2,484 oto-endoscopic images (OEIs) and classified them into one of three categories: normal, chronic otitis media (COM) with TM perforation, and otitis media with effusion (OME). Our results indicate that CNN models have significant potential for the automatic recognition of TM and ME infections, demonstrating a competitive accuracy of 95% in classifying TM and middle ear effusion (MEE) from OEIs. In addition to accuracy measurement, our approach achieves nearly perfect measures of 0.99 in terms of the average area under the receiver operating characteristics curve (AUROC). All these results indicate robust performance when recognizing TM and ME effusions in OEIs. Visualization through a class activation mapping (CAM) heatmap demonstrates that our proposed model performs prediction based on the correct region of OEIs. All these outcomes ensure the reliability of our method; hence, the study can aid otolaryngologists and primary care physicians in real-world scenarios. (c) 2020 Elsevier Ltd. All rights reserved.
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Park, Il-Ho
Guro Hospital (Department of Otorhinolaryngology-Head and Neck Surgery, Guro Hospital)
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