A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)
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TLDR
In this paper, a review of deep learning based systems for the detection of the new coronavirus (COVID-19) outbreak has been presented, which can be potentially further utilized to combat the outbreak.Abstract:
Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. The aim of this paper is to facilitate experts (medical or otherwise) and technicians in understanding the ways deep learning techniques are used in this regard and how they can be potentially further utilized to combat the outbreak of COVID-19.read more
Citations
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Journal ArticleDOI
Deep learning for COVID-19 detection based on CT images.
Wentao Zhao,Wei Jiang,Xinguo Qiu +2 more
TL;DR: In this article, a convolutional neural network (CNN) was adopted for CT image classification in COVID-19 testing and achieved the state-of-the-art performance.
Journal ArticleDOI
Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic
TL;DR: The contributions of deep learning at several scales including medical imaging, disease tracing, analysis of protein structure, drug discovery, and virus severity and infectivity to control the ongoing outbreak is discussed.
Posted ContentDOI
Diagnosis of COVID-19 from X-rays Using Combined CNN-RNN Architecture with Transfer Learning
TL;DR: A combined architecture of convolutional neural network (CNN) and recurrent Neural network (RNN) to diagnose COVID-19 from chest X-rays achieves promising results compared to other existing systems and might be validated in the future when more samples would be available.
Journal ArticleDOI
A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data
Matloob Khushi,Kamran Shaukat,Talha Mahboob Alam,Ibrahim A. Hameed,Shahadat Uddin,Suhuai Luo,Xiaoyan Yang,Maranatha Consuelo Reyes +7 more
TL;DR: In this article, the performance of 23 class imbalance methods (resampling and hybrid systems) with three classical classifiers (logistic regression, random forest, and LinearSVC) was used to identify the best imbalance techniques suitable for medical datasets.
Journal ArticleDOI
Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients.
Fahime Khozeimeh,Danial Sharifrazi,Navid Hoseini Izadi,Javad Hassannataj Joloudari,Afshin Shoeibi,Afshin Shoeibi,Roohallah Alizadehsani,Juan Manuel Górriz,Juan Manuel Górriz,Sadiq Hussain,Zahra Alizadeh Sani,Hossein Moosaei,Abbas Khosravi,Saeid Nahavandi,Sheikh Mohammed Shariful Islam,Sheikh Mohammed Shariful Islam,Sheikh Mohammed Shariful Islam +16 more
TL;DR: In this article, the authors proposed a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a convolutional neural network trained with clinical information.
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