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Research Article
Zhenzhang Li
Guangdong Polytechnic Normal University
Corresponding Author
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Early detection and appropriate medical treatment is of great use for ear disease. However, a new diagnostic strategy is necessary in the absence of experts and relatively low diagnostic accuracy, in which deep learning plays an important role. This paper puts forward a mechanic learning model which uses abundant otoscope image data gained in the clinical cases in order to achieve automatic diagnosis of ear diseases in real time. A total of 20,542 endoscopic images were employed to train nine common deep convolution neural networks. According to the characteristics of eardrum and external auditory canal, eight kinds of ear diseases were classified, involving the majority of ear diseases, such as normal, Cholestestoma of middle ear, Chronic suppurative otitis media, External auditory cana bleeding, Impacted cerumen, Otomycosis external, Secretory otitis media, Tympanic membrane calcification. After we evaluate these optimization schemes, two best performance models are selected to combine the ensemble classifiers with real-time automatic classification. Based on accuracy and training time, we choose a transferring learning model based on DensNet-BC169 and DensNet-BC1615, getting a result that each model has obvious improvement by using these two ensemble classifiers, and has average accuracy of 95.59%. Considering the dependence of classifier performance on data size in transfer learning, we evaluate the high accuracy of the current model that can be attributed to large databases. Current studies are unparalleled regarding disease diversity and diagnostic precision. The real-time classifier trains the data under different acquisition conditions, which is suitable for the real cases. According to this study, in the clinical case, deep learning model is of great use in early detection and remedy of ear diseases.
Keywords
deep learning model, ear diseases
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No competing interests reported.
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CURRENT STATUS: Under Revision
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Editorial decision: Major revision
On 13 May, 2021
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Received 26 Mar, 2021
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On 28 Feb, 2021
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Zhenzhang Li
Guangdong Polytechnic Normal University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 15 Feb, 2021
No community comments so far
Editorial decision: Major revision
On 13 May, 2021
Reviews received
Received 26 Mar, 2021
Reviews received
Received 13 Mar, 2021
Reviewers agreed
On 03 Mar, 2021
Reviewers agreed
On 28 Feb, 2021
Reviewers invited
Invitations sent on 28 Feb, 2021
Editor assigned
On 12 Feb, 2021
Editor invited
On 12 Feb, 2021
Submission checks complete
On 12 Feb, 2021
First submitted
On 26 Jan, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Early detection and appropriate medical treatment is of great use for ear disease. However, a new diagnostic strategy is necessary in the absence of experts and relatively low diagnostic accuracy, in which deep learning plays an important role. This paper puts forward a mechanic learning model which uses abundant otoscope image data gained in the clinical cases in order to achieve automatic diagnosis of ear diseases in real time. A total of 20,542 endoscopic images were employed to train nine common deep convolution neural networks. According to the characteristics of eardrum and external auditory canal, eight kinds of ear diseases were classified, involving the majority of ear diseases, such as normal, Cholestestoma of middle ear, Chronic suppurative otitis media, External auditory cana bleeding, Impacted cerumen, Otomycosis external, Secretory otitis media, Tympanic membrane calcification. After we evaluate these optimization schemes, two best performance models are selected to combine the ensemble classifiers with real-time automatic classification. Based on accuracy and training time, we choose a transferring learning model based on DensNet-BC169 and DensNet-BC1615, getting a result that each model has obvious improvement by using these two ensemble classifiers, and has average accuracy of 95.59%. Considering the dependence of classifier performance on data size in transfer learning, we evaluate the high accuracy of the current model that can be attributed to large databases. Current studies are unparalleled regarding disease diversity and diagnostic precision. The real-time classifier trains the data under different acquisition conditions, which is suitable for the real cases. According to this study, in the clinical case, deep learning model is of great use in early detection and remedy of ear diseases.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
No competing interests reported.
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