Detection of COVID-19 by Cough Sound: A Method Based on DSC + BiLSTM

In recent years, the outbreak of COVID-19 has brought a new round of challenges to global health care, and daily large-scale testing has also increased the consumption of medical resources. However, studies have shown that the cough sounds of patients with COVID-19 are significantly different from other Characteristics of respiratory infectious diseases. Therefore, this paper considers the use of the patient's cough as a detection sample to give the preliminary screening results. The research was conducted on the COUGHVID dataset. The experiment is divided into two stages: (1) Preprocessing stage: use Pitch Shift and Time Stretch to perform data enhancement on audio data, and use spec Augment to perform data enhancement on mel spectrogram. (2) Model construction stage: use two layers of DSC and one layer of BILSTM to splicing to obtain a classification model. Finally, the method is compared with the baseline method using only two layers of LSTM. The results show that accuracy has increased by 1.9%, F1 has increased by 1.9%, and AUC has increased by 1.6%. © 2022 IEEE.

Multi category mask wearing detection based on dynamic weighted category balance loss

Mask wearing in public has become an important measure to control the spread of Coronavirus Disease 2019 (COVID-19). With the prolonged development of the COVID-19 epidemic, the public’s awareness of self-protection has been gradually declining, leading to the increasing tendency of wearing masks incorrectly in public. The existing mask wearing detection methods usually only detect whether the mask is worn, without the detection of non-standard mask wearing scenarios, which is likely to cause cross infection. The current mask datasets lack the image data of non-standard mask wearing. To solve the above problems, on the basis of the existing mask datasets, more non-standard mask wearing images were collected through the Internet and offline, and the Mosaic data enhancement algorithm was improved to expand the data according to the features of face images in the cases of wearing masks. The improved Mosaic data enhancement algorithm could improve the mean average precision (mAP) of the benchmark network YOLOv4 by 2.08%. To address the problem of category imbalance in the dataset after data enhancement, the dynamic weighted balance loss function was proposed. Based on the weight binary cross entropy loss function, the reciprocal of the number of effective samples served as the auxiliary category weight, and dynamic adjustment was performed in each batch under training, thus solving the problems of weak stability, precision oscillation, and unsatisfactory effect when the re-weighting method was directly put to use. The experiment showed that mAP of the improved model reached 91.25%, and the average precision (AP) of non-standard mask wearing reached 91.69%. Compared with such single-stage methods as RetinaNet, Centernet, and Effcientdet, and such two-stage methods as YOLOv3-MobileNetV2 and YOLOv4-MobileNetV2, the improved algorithm exhibits higher detection accuracy and speed. © 2022, Editorial of Board of Journal of Graphics. All rights reserved.

DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images.

(1) Background: COVID-19 has been global epidemic. This work aims to extract 3D infection from COVID-19 CT images; (2) Methods: Firstly, COVID-19 CT images are processed with lung region extraction and data enhancement. In this strategy, gradient changes of voxels in different directions respond to geometric characteristics. Due to the complexity of tubular tissues in lung region, they are clustered to the lung parenchyma center based on their filtered possibility. Thus, infection is improved after data enhancement. Then, deep weighted UNet is established to refining 3D infection texture, and weighted loss function is introduced. It changes cost calculation of different samples, causing target samples to dominate convergence direction. Finally, the trained network effectively extracts 3D infection from CT images by adjusting driving strategy of different samples. (3) Results: Using Accuracy, Precision, Recall and Coincidence rate, 20 subjects from a private dataset and eight subjects from Kaggle Competition COVID-19 CT dataset tested this method in hold-out validation framework. This work achieved good performance both in the private dataset (99.94-00.02%, 60.42-11.25%, 70.79-09.35% and 63.15-08.35%) and public dataset (99.73-00.12%, 77.02-06.06%, 41.23-08.61% and 52.50-08.18%). We also applied some extra indicators to test data augmentation and different models. The statistical tests have verified the significant difference of different models. (4) Conclusions: This study provides a COVID-19 infection segmentation technology, which provides an important prerequisite for the quantitative analysis of COVID-19 CT images.