Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach.
Sara Hosseinzadeh Kassania,Peyman Hosseinzadeh Kassanib,Michal J. Wesolowskic,Kevin A. Schneidera,Ralph Detersa +4 more
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TLDR
In this article, the authors compared deep learning-based feature extraction frameworks for automatic COVID-19 classification, and found that the DenseNet121 feature extractor with Bagging tree classifier achieved the best performance with 99% classification accuracy.Citations
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COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques
Shanmuga Vadivel Kogilavani,J Prabhu,R. Sandhiya,M. Sandeep Kumar,Umashankar Subramaniam,Alagar Karthick,M. Muhibbullah,Sharmila Banu Sheik Imam +7 more
TL;DR: The results show that the VGG16 architecture gives better accuracy compared to other architectures for COVID-19, a novel pandemic that has emerged as a pandemic in recent years.
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Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision
TL;DR: In this article , a computer aided diagnosis model Cov-Net is proposed for accurate recognition of COVID-19 from chest X-ray images via machine vision techniques, which mainly concentrates on powerful and robust feature learning ability.
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Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients
TL;DR: In this article , the authors proposed a novel detection and classification approach (DCCNet) for quick diagnosis of COVID-19 using chest X-ray images of patients using CNN and histogram of oriented gradients (HOG).
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Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance.
Osama Shahid,Mohammad Nasajpour,Seyedamin Pouriyeh,Reza M. Parizi,Meng Han,Maria Valero,Fangyu Li,Mohammed Aledhari,Quan Z. Sheng +8 more
TL;DR: A comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus can be found in this article, where the authors present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective.
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A Comprehensive Survey of COVID-19 Detection Using Medical Images.
Faisal Muhammad Shah,Sajib Kumar Saha Joy,Farzad Ahmed,Tonmoy Hossain,Mayeesha Humaira,Amit Saha Ami,Shimul Paul,Abidur Rahman Khan Jim,Sifat Ahmed +8 more
TL;DR: In this paper, the authors reviewed some of the newly emerging AI-based models that can detect COVID-19 from X-ray or CT of lung images and inspected a total of 80 papers till June 20, 2020.
References
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Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
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TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
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