AIMC Topic: COVID-19 Testing
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Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images.
IEEE/ACM transactions on computational biology and bioinformatics
Oct 10, 2022
Accurate and rapid diagnosis of coronavirus disease 2019 (COVID-19) from chest CT scans is of great importance and urgency during the worldwide outbreak. However, radiologists have to distinguish COVID-19 pneumonia from other pneumonia in a large num...
Point-of-care SARS-CoV-2 sensing using lens-free imaging and a deep learning-assisted quantitative agglutination assay.
Lab on a chip
Sep 27, 2022
The persistence of the global COVID-19 pandemic caused by the SARS-CoV-2 virus has continued to emphasize the need for point-of-care (POC) diagnostic tests for viral diagnosis. The most widely used tests, lateral flow assays used in rapid antigen tes...
COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention.
Computers in biology and medicine
Sep 1, 2022
Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA) network is proposed via chest X-ray (CXR) image classification. First, to overcome the data shortage and improve the robustness of our network, a pixel-level image...
Selective Electrochemical Detection of SARS-CoV-2 Using Deep Learning.
Viruses
Aug 30, 2022
COVID-19 has been in the headlines for the past two years. Diagnosing this infection with minimal false rates is still an issue even with the advent of multiple rapid antigen tests. Enormous data are being collected every day that could provide insig...
Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model.
Computational intelligence and neuroscience
Aug 13, 2022
Due to the COVID-19 pandemic, computerized COVID-19 diagnosis studies are proliferating. The diversity of COVID-19 models raises the questions of which COVID-19 diagnostic model should be selected and which decision-makers of healthcare organizations...
DAFLNet: Dual Asymmetric Feature Learning Network for COVID-19 Disease Diagnosis in X-Rays.
Computational and mathematical methods in medicine
Aug 9, 2022
COVID-19 has become the largest public health event worldwide since its outbreak, and early detection is a prerequisite for effective treatment. Chest X-ray images have become an important basis for screening and monitoring the disease, and deep lear...
An Empirical Analysis of an Optimized Pretrained Deep Learning Model for COVID-19 Diagnosis.
Computational and mathematical methods in medicine
Jul 27, 2022
As a result of the COVID-19 outbreak, which has put the world in an unprecedented predicament, thousands of people have died. Data from structured and unstructured sources are combined to create user-friendly platforms for clinicians and researchers ...
COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence.
Computational intelligence and neuroscience
Jul 14, 2022
COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as pos...
Novel COVID-19 Diagnosis Delivery App Using Computed Tomography Images Analyzed with Saliency-Preprocessing and Deep Learning.
Tomography (Ann Arbor, Mich.)
Jun 20, 2022
This app project was aimed to remotely deliver diagnoses and disease-progression information to COVID-19 patients to help minimize risk during this and future pandemics. Data collected from chest computed tomography (CT) scans of COVID-19-infected pa...
PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data.
Computers in biology and medicine
Jun 7, 2022
While the advanced diagnostic tools and healthcare management protocols have been struggling to contain the COVID-19 pandemic, the spread of the contagious viral pathogen before the symptom onset acted as the Achilles' heel. Although reverse transcri...
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