Multi-Joint Unit Network for COVID-19 Diagnosis using X-Ray and CT Images

The global COVID-19 pandemic has caused a health crisis globally. Automated diagnostic methods can control the spread of the pandemic, as well as assists physicians to tackle high workload conditions through the quick treatment of affected patients. Owing to the scarcity of medical images and from different resources, the present image heterogeneity has raised challenges for achieving effective approaches to network training and effectively learning robust features. We propose a multi-joint unit network for the diagnosis of COVID-19 using the joint unit module, which leverages the receptive fields from multiple resolutions for learning rich representations. Existing approaches usually employ a large number of layers to learn the features, which consequently requires more computational power and increases the network complexity. To compensate, our joint unit module extracts low-, same-, and high-resolution feature maps simultaneously using different phases. Later, these learned feature maps are fused and utilized for classification layers. We observed that our model helps to learn sufficient information for classification without a performance loss and with faster convergence. We used three public benchmark datasets to demonstrate the performance of our network. Our proposed network consistently outperforms existing state-of-the-art approaches by demonstrating better accuracy, sensitivity, and specificity and F1-score across all datasets. © 2022 ACM.

Research on CT image classification algorithm of COVID-19 based on improved ResNet

The classification of COVID-19 and other viral pneumonias will help doctors to diagnose new coronary patients more accurately and quickly. Aiming at the classification problem of CT in patients with COVID-19, this paper proposes a CT image classification method based on an improved ResNet50 network based on the traditional convolutional neural network classification model. This paper uses the multiscale feature fusion strategy, combined with the improved attention mechanism to obtain the correlation coefficient between the internal feature points of the feature map, and finally achieves the effect of enhancing the representation ability of the feature map. Through the analysis and comparison of the technical principle, classification accuracy, and other parameters, it shows that the improved algorithm has better adaptive ability and classification ability. Through experiments, the improved ResNet50 classification model has a certain improvement in accuracy, time complexity, and spatial complexity compared with the traditional classification model, and the accuracy rate can reach 90.1 %. © 2022 Copyright for this paper by its authors.

Two-Stage COVID19 Classification Using BERT Features

We propose an automatic COVID1-19 diagnosis framework from lung CT-scan slice images using double BERT feature extraction. In the first BERT feature extraction, A 3D-CNN is first used to extract CNN internal feature maps. Instead of using the global average pooling, a late BERT temporal pooing is used to aggregate the temporal information in these feature maps, followed by a classification layer. This 3D-CNN-BERT classification network is first trained on sampled fixed number of slice images from every original CT scan volume. In the second stage, the 3D-CNN-BERT embedding features are extracted for every 32 slice images sequentially, and these features are divided into fixed number of segments. Then another BERT network is used to aggregate these features into a single feature followed by another classification layer. The classification results of both stages are combined to generate final outputs. On the validation dataset, we achieve macro F1 score 92.05%;and on the testing dataset, we achieve macro F1 84.43%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Res-SE-ConvNet: A Deep Neural Network for Hypoxemia Severity Prediction for Hospital In-Patients Using Photoplethysmograph Signal.

Determining the severity level of hypoxemia, the scarcity of saturated oxygen (SpO2) in the human body, is very important for the patients, a matter which has become even more significant during the outbreak of Covid-19 variants. Although the widespread usage of Pulse Oximeter has helped the doctors aware of the current level of SpO2 and thereby determine the hypoxemia severity of a particular patient, the high sensitivity of the device can lead to the desensitization of the care-givers, resulting in slower response to actual hypoxemia event. There has been research conducted for the detection of severity level using various parameters and bio-signals and feeding them in a machine learning algorithm. However, in this paper, we have proposed a new residual-squeeze-excitation-attention based convolutional network (Res-SE-ConvNet) using only Photoplethysmography (PPG) signal for the comfortability of the patient. Unlike the other methods, the proposed method has outperformed the standard state-of-art methods as the result shows 96.5% accuracy in determining 3 class severity problems with 0.79 Cohen Kappa score. This method has the potential to aid the patients in receiving the benefit of an automatic and faster clinical decision support system, thus handling the severity of hypoxemia.

Predicting the Stages of Covid-19 Affected Patients Using CNN with CT Scan

Battling the progressing Covid sickness 2019 (COVID-19) pandemic requests precise, quick, and point-of-care testing with quick outcomes to anticipate stages for isolation and therapy. The preliminary test to detect COVID-19 is a Swab test and also a Blood test, but these tests will take more than 2 days to receive the results and there is also a risk of transmission of the virus while collecting the samples. To predict the stages of COVID-19's effects on the human lungs accurately for further treatment for further diagnosis on a radiological image, medical experts need a high level of precision. We utilize image processing techniques and convolutional networks to analyze CT images of COVID-19 affected human lungs in this paper for the detection of pulmonary abnormalities in the early stage, Chest X-Ray is not exact. So, we are using Computed Tomography (CT) imaging especially for identifying the stages of lung anomalies. We present and discuss the scoring systems which cause the severity in lungs of COVID-19 patients every day. This will be accurate for predicting the stages of COVID-19 for early treatment and also to protect the uninfected population. © 2022 IEEE.

Impact of Multi-size Small Anchors and Penalized IoU on Face Mask Detection

Wearing masks as one of the most effective ways to diminish the transmission of COVID-19 increases the demand for automatic face mask detection in all countries. Face masks belong to the small objects category in images, thus introducing the challenge of training a robust face mask detector, particularly for small object detection. Feature Pyramids derived from deep convolutional neural networks are commonly used to achieve scale-invariant object detection;however, it does not reach the same level of performance in detecting face masks as in detecting larger objects. This work proposed two methods: fully utilizing the feature map extract from the neural network by adding small multiscale anchors on the last feature map, which contains the highest resolution information. The other is to replace the standard IoU calculation with a tolerant strategy for small objects. Using these two methods, we improve the accuracy of small object detection while increasing the general average precision. © 2021 IEEE.

Localized Feature Aggregation Module for Semantic Segmentation

We propose a new information aggregation method which called "Localized Feature Aggregation Module"based on the similarity between the feature maps of an encoder and a decoder. The proposed method recovers positional information by emphasizing the similarity between decoder's feature maps with superior semantic information and encoder's feature maps with superior positional information. The proposed method can learn positional information more efficiently than conventional con-catenation in the U-net and attention U-net. Additionally, the proposed method also uses localized attention range to reduce the computational cost. Two innovations contributed to improve the segmentation accuracy with lower computational cost. By experiments on the Drosophila cell image dataset and COVID-19 image dataset, we confirmed that our method outperformed conventional methods. © 2021 IEEE.

LRSAR-Net Semantic Segmentation Model for Computer Aided Diagnosis for Covid-19 CT Image

Since the outbreak of the Covid-19 epidemic in the world in late 2019, all countries in the world are under the threat of epidemic. Covid-19 invades the body's respiratory system, causing lung infection or even death. Computed Tomography (CT) is a routine method for doctors to diagnose patients with pneumonia. In order to improve the efficiency of doctors in diagnosing patients with new crown infection, this paper proposes a semantic segmentation network LRSAR-Net based on low rank tensor self-attention reconstruction, in which the low rank tensor self-attention reconstruction module is used to obtain long-range information. The low rank tensor self-attention reconstruction module mainly includes three parts: low rank tensor generation sub module, low rank self-attention sub module and high rank tensor reconstruction module. The low rank tensor self-attention module is divided into multiple low rank tensors, the low rank self-attention feature map is constructed, and then the multiple low rank tensor attention feature maps are reconstructed into a high rank attention feature map. The self-attention module obtains long-range semantic information by calculating the similarity matrix. Compared with the traditional self-attention module Non Local, the low rank tensor self-attention reconstruction module has lower computational complexity and faster computing speed. Finally, this paper compares with other excellent semantic segmentation models to reflect the effectiveness of the model. © 2022, Science Press. All right reserved.

Machine Learning-Backed Planning of Rapid COVID-19 Tests With Autonomous Vehicles With Zero-Day Considerations

The COVID-19 pandemic has stretched public health resources to the limits, and the only realistic way to keep the infection rates low is effective testing to prevent community transmission. In this research study, we propose an innovative method to empower autonomous vehicle-driven mobile assessment facilities to support early detection of the cases contracted with the virus, and enable early detection of sources for hot spots. We describe a self organizing feature map (SOFM) approach to the allocation of the mobile assessment centers, and also use the same method to determine the travel route of the autonomous vehicles, and provide critical decision support to the supply chain manager. Our results reveal that the optimal number of neurons under varying test times can be obtained by 5 different zero-day coordinates of initially contracted cases and worst-case scenario to find out the contracted cases in 17 days and 27 days under two different test time scenarios.

Covid-19 Classification with Deep Neural Network and Belief Functions

Computed tomography (CT) image provides useful information for radiologists to diagnose Covid-19. However, visual analysis of CT scans is time-consuming. Thus, it is necessary to develop algorithms for automatic Covid-19 detection from CT images. In this paper, we propose a belief function-based convolutional neural network with semi-supervised training to detect Covid-19 cases. Our method first extracts deep features, maps them into belief degree maps and makes the final classification decision. Our results are more reliable and explainable than those of traditional deep learning-based classification models. Experimental results show that our approach is able to achieve a good performance with an accuracy of 0.81, an F1 of 0.812 and an AUC of 0.875. © 2021 ACM.