Automatic Detection of Tympanic Membrane and Middle Ear Infection from Oto-Endoscopic Images via Convolutional Neural Networks

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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.
Publisher
Pergamon Press Ltd.
Issue Date
2020-06
Language
English
Article Type
Article
Citation

Neural Networks, v.126, pp.384 - 394

ISSN
0893-6080
DOI
10.1016/j.neunet.2020.03.023
URI
http://hdl.handle.net/10203/279481
Appears in Collection
RIMS Journal Papers
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