عنوان انگلیسی مقاله:
Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database
ترجمه فارسی عنوان مقاله:
تشخیص خودکار بیماری گوش با استفاده از یادگیری عمیق گروه با یک پایگاه داده بزرگ تصویر otoendoscopy
Sciencedirect - Elsevier - EBioMedicine 45 (2019) 606–614
Dongchul Cha a,1, Chongwon Pae b,c,d,1, Si-Baek Seong b,c, Jae Young Choi a,c,⁎⁎, Hae-Jeong Park b,c,d,⁎
Background: Ear and mastoid disease can easily be treated by early detection and appropriate medical care. However,
short of specialists and relatively lowdiagnostic accuracy calls for a newway of diagnostic strategy, inwhich
deep learning may play a significant role. The current study presents a machine learning model to automatically
diagnose ear disease using a large database of otoendoscopic images acquired in the clinical environment.
Methods: Total 10,544 otoendoscopic images were used to train nine public convolution-based deep neural networks
to classify eardrum and external auditory canal features into six categories of ear diseases, covering most
ear diseases (Normal, Attic retraction, Tympanic perforation, Otitis externa±myringitis, Tumor). After evaluating
several optimization schemes, two best-performingmodelswere selected to compose an ensemble classifier, by
combining classification scores of each classifier.
Findings: According to accuracy and training time, transfer learning models based on Inception-V3 and
ResNet101 were chosen and the ensemble classifier using the two models yielded a significant improvement
over each model, the accuracy of which is in average 93·67% for the 5-folds cross-validation. Considering substantial
data-size dependency of classifier performance in the transfer learning, evaluated in this study, the
high accuracy in the current model is attributable to the large database.
Interpretation: The current study is unprecedented in terms of both disease diversity and diagnostic accuracy,
which is compatible or even better than an average otolaryngologist. The classifier was trainedwith data in a various
acquisition condition,which is suitable for the practical environment. This study shows the usefulness of utilizing
a deep learning model in the early detection and treatment of ear disease in the clinical situation.
Fund: This research was supported by Brain Research Program through the National Research Foundation of
Korea (NRF) funded by the Ministry of Science and ICT(NRF-2017M3C7A1049051).
Keywords: Convolutional neural network | Deep learning | Otoendoscopy | Tympanic membrane | Ear disease | Ensemble learning