摘要
中耳炎是常见的耳科疾病之一,其准确诊断能够预防传导性听力损伤的恶化,以及避免抗生素的过度使用。目前中耳炎诊断主要由医生依据耳镜设备反馈的图像进行目视检查。因耳镜设备图片拍摄质量及医生诊断经验的影响,该主观检查存在较大的误诊率。针对该问题,本文提出采用快速区域卷积神经网络对临床采集的数字耳镜影像进行分析。首先,通过图像数据增强和预处理,扩充了临床耳镜数据集样本数量。然后,根据耳镜图片特征针对性地筛选出卷积神经网络进行特征提取,同时引入特征金字塔网络以进行多尺度的特征提取,增强检测能力。最后,采用锚框尺度优化和超参数调整的快速卷积神经网络进行识别,并通过随机选取的测试集检验该方法的有效性。结果显示,在测试样本中耳镜图片的总体识别准确率达到91.43%。以上研究表明,所提方法有效提高了耳镜图片分类的准确率,有望辅助临床诊断。
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.
作者
卢硕辰
刘后广
杨建华
刘送永
周雷
黄新生
LU Shuochen;LIU Houguang;YANG Jianhua;LIU Songyong;ZHOU Lei;HUANG Xinsheng(School of Mechatronic Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221116,P.R.China;Department of Otorhinolaryngology,Zhongshan Hospital Affiliated to Fudan University,Shanghai 200032,P.R.China)
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2021年第6期1054-1061,1071,共9页
Journal of Biomedical Engineering
基金
国家自然科学基金资助项目(51775547)
上海市科学技术委员会基金(17411962200)
江苏高校品牌专业建设工程资助项目
江苏高校优势学科建设工程资助项目。
关键词
中耳炎
深度学习
卷积神经网络
目标检测
计算机辅助诊断
otitis media
deep learning
convolutional neural network
object detection
computer-aided diagnosis
作者简介
通信作者:刘后广,Email:liuhg@cumt.edu.cn。
