Research on computer aided diagnosis of otitis media based on faster region convolutional neural network.
10.7507/1001-5515.202009022
- Author:
Shuochen LU
1
;
Houguang LIU
1
;
Jianhua YANG
1
;
Songyong LIU
1
;
Lei ZHOU
2
;
Xinsheng HUANG
2
Author Information
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.
- Publication Type:Journal Article
- Keywords:
computer-aided diagnosis;
convolutional neural network;
deep learning;
object detection;
otitis media
- MeSH:
Computers;
Diagnosis, Computer-Assisted;
Humans;
Neural Networks, Computer;
Otitis Media/diagnosis*
- From:
Journal of Biomedical Engineering
2021;38(6):1054-1061
- CountryChina
- Language:Chinese
-
Abstract:
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.
