Sensorineural hearing loss in patients with coronary artery bypass surgery

Background: This study is planned to obtain a better understanding of the correlation between sudden sensorineural hearing loss and cardiopulmonary bypass. There are many causes for sudden hearing loss which include infectious, circulatory, inner ear problems like meniere′s disease, neoplastic, traumatic, metabolic, neurologic, immunologic, toxic, cochlear, idiopathic (unknown cause) and other causes. One of the less common cause is surgery include cardiopulmonary bypass procedures. Materials and Methods: This study is a self controlled clinical trial on 105 patients that was carried out in chamran Hospital, Esfahan, Iran. Participants were including all those patients undergoing coronary artery bypass surgery in the hospital who fell under the criteria for inclusion. Patients underwent audiometric testing at our hospital on three or two different occasions during the course of this study, Initially before the procedure to test the baseline hearing capacity; then two week after the procedure to assess any changes in hearing ability following the surgery. Data analysis performed by co-variance analysis. Results: In our study the changes in the threshold of hearing in frequency of 1000 in right ear and in frequencies of 2000 and 4000 in left ear were significant, but this changes were about 2-3 db and were not noticeable. The difference in degree of SNHL, before and after surgery in different frequencies were been shown. Conclusion: As loss of the patients with symptomatic sensory neural hearing loss in this study, It isn′t commanded the routin auditory assessment pre and post surgery was been done

Dual-Branch Fusion of Convolutional Neural Network and Graph Convolutional Network for PolSAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) images contain useful information, which can lead to extensive land cover interpretation and a variety of output products. In contrast to optical imagery, there are several challenges in extracting beneficial features from PolSAR data. Deep learning (DL) methods can provide solutions to address PolSAR feature extraction challenges. The convolutional neural networks (CNNs) and graph convolutional networks (GCNs) can drive PolSAR image characteristics by deploying kernel abilities in considering neighborhood (local) information and graphs in considering long-range similarities. A novel dual-branch fusion of CNN and mini-GCN is proposed in this study for PolSAR image classification. To fully utilize the PolSAR image capacity, different spatial-based and polarimetric-based features are incorporated into CNN and mini-GCN branches of the proposed model. The performance of the proposed method is verified by comparing the classification results to multiple state-of-the-art approaches on the airborne synthetic aperture radar (AIRSAR) dataset of Flevoland and San Francisco. The proposed approach showed 1.3% and 2.7% improvements in overall accuracy compared to conventional methods with these AIRSAR datasets. Meanwhile, it enhanced its one-branch version by 0.73% and 1.82%. Analyses over Flevoland data further indicated the effectiveness of the dual-branch model using varied training sampling ratios, leading to a promising overall accuracy of 99.9% with a 10% sampling ratio