摘要
现有的良性阵发性位置性眩晕视频眼震图分类算法存在以下不足:人工提取的特征主观性和局限性强;眼球的轴向转动特征提取困难;仅能区分正常人群和患者,或对简单的眼震进行分类。针对上述问题,提出了一种基于注意力机制的视频眼震图分类算法。以轻量级模型三维MobileNet V2为基础网络进行特征提取,在全局细节特征、时空信息丰富的网络低层引入全局时空注意力模块,融合眼球震颤空间信息和帧间时序信息;在网络高层引入时空通道注意力机制,筛选高级语义特征;采用带有类别调制系数的交叉熵损失函数对网络进行训练,有效缓解了类别数量不平衡的问题。在复旦大学附属眼耳鼻喉科医院提供的包括66种类别的视频眼震图数据集上进行了实验,所提算法的分类准确度达到90.08%,各类别的平均精准度、召回率、F1-score分别为90.50%,92.00%,90.40%,表明了所提算法的优越性。
The existing classification algorithms for benign paroxysmal positional vertigo video nystagmus have the following shortcomings.The features extracted manually are subjective and limited;the feature extraction of axial rotation of eyeballs is difficult;it can only distinguish between normal people and patients or classify simple nystagmus.To overcome the above shortcomings,a video nystagmus classification algorithm based on attention mechanism is proposed.Based on the lightweight model three-dimensional MobileNet V2,a network is used for feature extraction,and the global spatiotemporal attention module is introduced at the lower level of the network with rich global detail features and spatiotemporal information to integrate the spatial information of nystagmus and the temporal information between frames.The attention mechanism of the spatiotemporal channel is introduced to the high-level network to screen high-level semantic features.The cross entropy loss function with category modulation coefficient is used to train the network,which effectively alleviates the problem of imbalance in several categories.Experiments were conducted on 66 types of video nystagmus datasets provided by the Eye and ENT Hospital of Fudan University.The classification accuracy of the proposed algorithm reached 90.08%,and the average accuracy,recall,and F1-score of each category were 90.50%,92.00%,and 90.40%,respectively,indicating the superiority of the proposed algorithm.
作者
周浩军
赵晓丽
高永彬
李海波
程若然
Zhou Haojun;Zhao Xiaoli;Gao Yongbin;Li Haibo;Cheng Ruoran(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201600,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第16期370-379,共10页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61772328)。
关键词
医用光学
图像处理
医学图像处理
视频眼震图分类
时空注意力机制
良性阵发性位置性眩晕
三维卷积神经网络
medical optics
image processing
medical image processing
video nystagmus classification
spatiotemporal attention mechanism
benign paroxysmal positional vertigo
three-dimensional convolutional neural network
