Research on computer aided diagnosis of otitis media based on faster region convolutional neural network_Journal of Biomedical Engineering

Authors：

LU Shuochen ¹ ,  LIU Houguang ¹ , YANG Jianhua ¹ , LIU Songyong ¹ , ZHOU Lei ² , HUANG Xinsheng ²

1. School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, P.R.China;
2. Department of Otorhinolaryngology, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, P.R.China;

Corresponding author：

LIU Houguang, Email: liuhg@cumt.edu.cn

Keywords：

otitis media; deep learning; convolutional neural network; object detection; computer-aided diagnosis

DOI：

10.7507/1001-5515.202009022

Video：

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Otitis media is one of the common ear diseases, and its accurate diagnosis can prevent the deterioration of conductive hearing loss and avoid the overuse of antibiotics. At present, the diagnosis of otitis media mainly relies on the doctor's visual inspection based on the images fed back by the otoscope equipment. Due to the quality of otoscope equipment pictures and the doctor's diagnosis experience, this subjective examination has a relatively high rate of misdiagnosis. In response to this problem, this paper proposes the use of faster region convolutional neural networks to analyze clinically collected digital otoscope pictures. First, through image data enhancement and preprocessing, the number of samples in the clinical otoscope dataset was expanded. Then, according to the characteristics of the otoscope picture, the convolutional neural network was selected for feature extraction, and the feature pyramid network was added for multi-scale feature extraction to enhance the detection ability. Finally, a faster region convolutional neural network with anchor size optimization and hyperparameter adjustment was used for identification, and the effectiveness of the method was tested through a randomly selected test set. The results showed that the overall recognition accuracy of otoscope pictures in the test samples reached 91.43%. The above studies show that the proposed method effectively improves the accuracy of otoscope picture classification, and is expected to assist clinical diagnosis.

Citation： LU Shuochen, LIU Houguang, YANG Jianhua, LIU Songyong, ZHOU Lei, HUANG Xinsheng. Research on computer aided diagnosis of otitis media based on faster region convolutional neural network. Journal of Biomedical Engineering, 2021, 38(6): 1054-1061. doi: 10.7507/1001-5515.202009022 Copy

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2.	American Academy Of Pediatrics Media. Diagnosis and management of acute otitis media. Pediatrics, 2004, 113(5): 1451-1465.
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14.	Myburgh H C, Van Zijl W H, Swanepoel D, et al. Otitis media diagnosis for developing countries using tympanic membrane image-analysis. EBioMedicine, 2016, 5: 156-160.
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22.	Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell, 2016, 39(6): 1137-1149.
23.	Zhang L, Lin L, Liang X, et al. Is faster R-CNN doing well for pedestrian detection?// European Conference on Computer Vision. Amsterdam: Springer, 2016: 443-457.
24.	鞠孟汐, 李欣蔚, 李章勇. 基于深度主动学习的白带白细胞智能检测方法研究. 生物医学工程学杂志, 2020, 37(3): 519-526.
25.	Salamon J, Bello J P. Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process Lett, 2017, 24(3): 279-283.
26.	Frid-Adar M, Diamant I, Klang E, et al. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing, 2018, 321: 321-331.
27.	Ke H, Chen D, Li X, et al. Towards brain big data classification: Epileptic EEG identification with a lightweight VGGNet on global MIC. IEEE Access, 2018, 6: 14722-14733.
28.	Wu Z, Shen C, Van Den Hengel A. Wider or deeper: Revisiting the resnet model for visual recognition. Pattern Recog, 2019, 90: 119-133.
29.	Yin W, Zhao C, Chen Y. Germplasm selection based on machine vision// 2019 7th International Conference on Information Technology: IoT and Smart City. Shanghai: Association for Computing Machinery, 2019: 234-237.
30.	Shin H, Roth H R, Gao M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging, 2016, 35(5): 1285-1298.
31.	Lin T, Dollár P, Girshick R, et al. Feature pyramid networks for object detection// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii: IEEE, 2017: 2117-2125.
32.	宋尚玲, 杨阳, 李夏, 等. 基于Faster-RCNN的肺结节检测算法. 中国生物医学工程学报, 2020, 39(2): 129-136.
33.	Kong T, Yao A, Chen Y, et al. Hypernet: Towards accurate region proposal generation and joint object detection// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 845-853.
34.	Ding P, Zhang Y, Deng W J, et al. A light and faster regional convolutional neural network for object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens, 2018, 141: 208-218.
35.	Fushiki T. Estimation of prediction error by using K-fold cross-validation. Statist Comput, 2011, 21(2): 137-146.
36.	Başaran E, Cömert Z, Çelik Y. Convolutional neural network approach for automatic tympanic membrane detection and classification. Biomed Signal Process Control, 2020, 56: 101734.

1. Shivani-Becker S, Carr M M. Current management and referral patterns of pediatricians for acute otitis media. Int J Pediatr Otorhinolaryngol, 2018, 113: 19-21.
2. American Academy Of Pediatrics Media. Diagnosis and management of acute otitis media. Pediatrics, 2004, 113(5): 1451-1465.
3. 杨仕明, 袁虎. 中耳炎的分类分型和诊治. 中华耳鼻咽喉头颈外科杂志, 2007, 42(7): 554-557.
4. Nyquist A, Gonzales R, Steiner J F, et al. Antibiotic prescribing for children with colds, upper respiratory tract infections, and bronchitis. JAMA, 1998, 279(11): 875-877.
5. Marom T, Kraus O, Habashi N, et al. Emerging technologies for the diagnosis of otitis media. Otolaryngol Head Neck Surg, 2019, 160(3): 447-456.
6. Goggin S L, Eikelboom R H, Atlas M D. Clinical decision support systems and computer-aided diagnosis in otology. Otolaryngol Head Neck Surg, 2007, 136(4): 21-26.
7. Sundvall P, Papachristodoulou C E, Nordeman L. Diagnostic methods for acute otitis media in 1 to 12 year old children: a cross sectional study in primary health care. BMC Fam Pract, 2019, 20(1): 127-134.
8. Blomgren K, Pitkäranta A. Is it possible to diagnose acute otitis media accurately in primary health care?. Family Practice, 2003, 20(5): 524-527.
9. 梁蒙蒙, 周涛, 张飞飞, 等. 卷积神经网络及其在医学图像分析中的应用研究. 生物医学工程学杂志, 2018, 35(6): 977-985.
10. 俞益洲, 石德君, 马杰超, 等. 人工智能在医学影像分析中的应用进展. 中国医学影像技术, 2019, 35(12): 1808-1812.
11. Heck V, Wangenheim A V, Abdala D D, et al. Computational techniques for accompaniment and measuring of otology pathologies// Twentieth IEEE International Symposium on Computer-Based Medical Systems. Maribor Slovenia: IEEE, 2007: 53-58.
12. Mironică I, Vertan C, Gheorghe D C. Automatic pediatric otitis detection by classification of global image features// 2011 E-Health and Bioengineering Conference (EHB). Iasi Romania: IEEE, 2011: 1-4.
13. Shie C, Chang H, Fan F, et al. A hybrid feature-based segmentation and classification system for the computer aided self-diagnosis of otitis media// 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago: IEEE, 2014: 4655-4658.
14. Myburgh H C, Van Zijl W H, Swanepoel D, et al. Otitis media diagnosis for developing countries using tympanic membrane image-analysis. EBioMedicine, 2016, 5: 156-160.
15. Myburgh H C, Jose S, Swanepoel D W, et al. Towards low cost automated smartphone- and cloud-based otitis media diagnosis. Biomed Signal Process Control, 2018, 39: 34-52.
16. Lemley J, Bazrafkan S, Corcoran P. Deep learning for consumer devices and services: Pushing the limits for machine learning, artificial intelligence, and computer vision. IEEE Consum Electron Mag, 2017, 6(2): 48-56.
17. 罗会兰, 陈鸿坤. 基于深度学习的目标检测研究综述. 电子学报, 2020, 48(6): 1230-1239.
18. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Commun ACM, 2017, 60(6): 84-90.
19. Başaran E, Şengür A, Cömert Z, et al. Normal and acute tympanic membrane diagnosis based on gray level co-occurrence matrix and artificial neural networks// 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). Malatya Turkey: IEEE, 2019: 1-6.
20. Viscaino M, Maass J C, Delano P H, et al. Computer-aided diagnosis of external and middle ear conditions: A machine learning approach. PLoS one, 2020, 15(3): 229226.
21. Everingham M, Van Gool L, Williams C K, et al. The pascal visual object classes (voc) challenge. Int J Comput Vis, 2010, 88(2): 303-338.
22. Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell, 2016, 39(6): 1137-1149.
23. Zhang L, Lin L, Liang X, et al. Is faster R-CNN doing well for pedestrian detection?// European Conference on Computer Vision. Amsterdam: Springer, 2016: 443-457.
24. 鞠孟汐, 李欣蔚, 李章勇. 基于深度主动学习的白带白细胞智能检测方法研究. 生物医学工程学杂志, 2020, 37(3): 519-526.
25. Salamon J, Bello J P. Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process Lett, 2017, 24(3): 279-283.
26. Frid-Adar M, Diamant I, Klang E, et al. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing, 2018, 321: 321-331.
27. Ke H, Chen D, Li X, et al. Towards brain big data classification: Epileptic EEG identification with a lightweight VGGNet on global MIC. IEEE Access, 2018, 6: 14722-14733.
28. Wu Z, Shen C, Van Den Hengel A. Wider or deeper: Revisiting the resnet model for visual recognition. Pattern Recog, 2019, 90: 119-133.
29. Yin W, Zhao C, Chen Y. Germplasm selection based on machine vision// 2019 7th International Conference on Information Technology: IoT and Smart City. Shanghai: Association for Computing Machinery, 2019: 234-237.
30. Shin H, Roth H R, Gao M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging, 2016, 35(5): 1285-1298.
31. Lin T, Dollár P, Girshick R, et al. Feature pyramid networks for object detection// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii: IEEE, 2017: 2117-2125.
32. 宋尚玲, 杨阳, 李夏, 等. 基于Faster-RCNN的肺结节检测算法. 中国生物医学工程学报, 2020, 39(2): 129-136.
33. Kong T, Yao A, Chen Y, et al. Hypernet: Towards accurate region proposal generation and joint object detection// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 845-853.
34. Ding P, Zhang Y, Deng W J, et al. A light and faster regional convolutional neural network for object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens, 2018, 141: 208-218.
35. Fushiki T. Estimation of prediction error by using K-fold cross-validation. Statist Comput, 2011, 21(2): 137-146.
36. Başaran E, Cömert Z, Çelik Y. Convolutional neural network approach for automatic tympanic membrane detection and classification. Biomed Signal Process Control, 2020, 56: 101734.

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Research on computer aided diagnosis of otitis media based on faster region convolutional neural network

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