Efficient and accurate identification of ear diseases using an ensemble deep learning model

• Published in: Scientific reports Vol. 11; no. 1; p. 10839
• Main Authors: Zeng, Xinyu, Jiang, Zifan, Luo, Wen, Li, Honggui, Li, Hongye, Li, Guo, Shi, Jingyong, Wu, Kangjie, Liu, Tong, Lin, Xing, Wang, Fusen, Li, Zhenzhang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25.05.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/1386; 631/114/1564; Accuracy; Adult; Calcification; Deep Learning; Ear canal; Ear diseases; Ear Diseases - diagnosis; Ear Diseases - pathology; Early Diagnosis; Endoscopy; Female; Humanities and Social Sciences; Humans; Image Interpretation, Computer-Assisted - methods; Male; Medical treatment; Middle ear; multidisciplinary; Neural networks; Neural Networks, Computer; Otitis media; Otomycosis; Science; Science (multidisciplinary); Sensitivity and Specificity; Severity of Illness Index; Transfer learning; Tympanic membrane; Young Adult
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-90345-w

Early detection and appropriate medical treatment are of great use for ear disease. However, a new diagnostic strategy is necessary for 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 clinical cases to achieve an 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 the eardrum and external auditory canal, eight kinds of ear diseases were classified, involving the majority of ear diseases, such as normal, Cholestestoma of the 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 an 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 real cases. According to this study, in the clinical case, the deep learning model is of great use in the early detection and remedy of ear diseases.