Diagnostic methods for nystagmus in vertigo based on non-local convolution and convolutional block attention module
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.
benign paroxysmal positional vertigomedical image processingtemporal data classificationobject detectionvideo nystagmus data classification
万方数据
维普
