In view of complex pathogenic factors and diagnostic difficulties of benign paroxysmal positional vertigo,a novel method for diagnosing nystagmus in vertigo based on non-local convolutional and convolutional block attention module(CBAM)is proposed.An object detection model is constructed to locate the pupil,thereby tracking eye movement and extract temporal data of horizontal and vertical motion trajectories.Subsequently,a classification model is employed for detection and classification,utilizing a non-local convolutional module to capture remote dependency relationships in nystagmus data,and introducing CBAM to extract high-and low-level semantic information in the feature layer for enhancing the detection performance.Experiments were conducted on a video nystagmus dataset provided by the Eye,Ear,Nose and Throat Hospital.The results show that compared with the best mainstream method,the proposed method improves precision,recall rate,accuracy,and average F1 score by 1.82%,2.09%,1.62%,and 1.96%,respectively,demonstrating its superiority.
关键词
良性阵发性位置性眩晕/医学图像处理/时序数据分类/目标检测/视频眼震数据分类
Key words
benign paroxysmal positional vertigo/medical image processing/temporal data classification/object detection/video nystagmus data classification
维普
万方数据