COVID-19 Screening Using Residual Attention Network an Artificial Intelligence Approach

@article{Sharma2020COVID19SU,
  title={COVID-19 Screening Using Residual Attention Network an Artificial Intelligence Approach},
  author={Vishal Chandra Sharma and Curtis E. Dyreson},
  journal={2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)},
  year={2020},
  pages={1354-1361}
}
  • V. Sharma, C. Dyreson
  • Published 26 June 2020
  • Medicine
  • 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)
Coronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2). The virus transmits rapidly; it has a basic reproductive number (R0) of 2.2−2.7. In March 2020, the World Health Organization declared the COVID-19 outbreak a pandemic. COVID-19 is currently affecting more than 200 countries with 6M active cases. An effective testing strategy for COVID-19 is crucial to controlling the outbreak but the demand for testing surpasses the availability… 

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References

SHOWING 1-10 OF 60 REFERENCES

Attention is All you Need

TLDR
A new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely is proposed, which generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

Residual Attention Network for Image Classification

TLDR
The proposed Residual Attention Network is a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion and can be easily scaled up to hundreds of layers.

Densely Connected Convolutional Networks

TLDR
The Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion, and has several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

TLDR
This work introduces two simple global hyper-parameters that efficiently trade off between latency and accuracy and demonstrates the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.

Going deeper with convolutions

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition

Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal

TLDR
This review indicates that proposed models for diagnosing coronavirus disease 2019 (covid-19) are poorly reported, at high risk of bias, and their reported performance is probably optimistic, and it is not recommend any of these reported prediction models for use in current practice.

2020

RETRACTED ARTICLE: Deep learning system to screen coronavirus disease 2019 pneumonia

TLDR
A study that compared multiple convolutional neural network models to classify CT samples with COVID-19, Influenza viral pneumonia, or no-infection, and achieved an AUC of 0.996 (95%CI: 0.989–1.00) for Coronavirus vs Non-coronav virus cases per thoracic CT studies is technically reviewed.

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

TLDR
A novel Joint Classification and Segmentation (JCS) system to perform real-time and explainable COVID- 19 chest CT diagnosis and extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for CO VID-19 classification and segmentation.
...