박해정 (연세대학교)

상위피인용논문

조회196

연세대학교

EBioMedicine, 2019 Jul:45:606-614 | https://doi.org/10.1016/j.ebiom.2019.06.050 Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database

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,⁎

^aDepartment of Otorhinolaryngology, Yonsei University College of Medicine, Republic of Korea
^bCenter for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Republic of Korea
^cBK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Republic of Korea
^dDepartment of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea

¹Equally contributed first authors.
^*Corresponding authors: correspondence to Jae Young Choi or Hae-Jeong Park

Abstract

Background: Ear and mastoid disease can easily be treated by early detection and appropriate medical care. However, short of specialists and relatively low diagnostic accuracy calls for a new way of diagnostic strategy, in which 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-performing models were 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 trained with 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).

논문정보

형식Research article
게재일2019년 07월 (BRIC 등록일 2024-03-25)
연구진국내 연구진
분야 의약학 > 응용의학
피인용횟수120회 이상 인용된 논문

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