EDL-COVID: Ensemble Deep Learning for COVID-19 Case Detection From Chest X-Ray Images
Shanjiang Tang,Chunjiang Wang,Jiangtian Nie,Neeraj Kumar,Yang Zhang,Zehui Xiong,Ahmed Barnawi +6 more
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TLDR
Experimental results show that EDL-COVID offers promising results for COVID-19 case detection with an accuracy of 95%, better than CO VID-Net of 93.3% and a proposed weighted averaging ensembling method that is aware of different sensitivities of deep learning models on different classes types.Abstract:
Effective screening of COVID-19 cases has been becoming extremely important to mitigate and stop the quick spread of the disease during the current period of COVID-19 pandemic worldwide. In this article, we consider radiology examination of using chest X-ray images, which is among the effective screening approaches for COVID-19 case detection. Given deep learning is an effective tool and framework for image analysis, there have been lots of studies for COVID-19 case detection by training deep learning models with X-ray images. Although some of them report good prediction results, their proposed deep learning models might suffer from overfitting, high variance, and generalization errors caused by noise and a limited number of datasets. Considering ensemble learning can overcome the shortcomings of deep learning by making predictions with multiple models instead of a single model, we propose EDL-COVID , an ensemble deep learning model employing deep learning and ensemble learning. The EDL-COVID model is generated by combining multiple snapshot models of COVID-Net, which has pioneered in an open-sourced COVID-19 case detection method with deep neural network processed chest X-ray images, by employing a proposed weighted averaging ensembling method that is aware of different sensitivities of deep learning models on different classes types. Experimental results show that EDL-COVID offers promising results for COVID-19 case detection with an accuracy of 95%, better than COVID-Net of 93.3%.read more
Citations
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Journal ArticleDOI
COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning
TL;DR: In this paper , the authors used deep convolutional neural networks in a large dataset of chest X-ray images to detect the COVID-19 pneumonia using the transfer learning paradigm.
Journal ArticleDOI
Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review
Yogesh H. Bhosale,K. S. Patnaik +1 more
TL;DR: DL-based Covid-19 detection systems are the key focus of this review article, evaluating causal agents, pathophysiology, immunological reactions, and epidemiological illness.
Journal ArticleDOI
Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays
TL;DR: In this article , an inverted bell-curve-based ensemble of deep learning models was proposed for the detection of COVID-19 from CXR images. But, the proposed method is not suitable for the classification of lung cancer.
Journal ArticleDOI
Machine learning applications for COVID-19 outbreak management
TL;DR: In this paper , the authors employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers, including survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps.
Journal ArticleDOI
PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates
TL;DR: PulDi-COVID as discussed by the authors is an ensemble DL model that combines DL with ensemble learning for COVID-19 detection, which achieved a 99.70% accuracy, 98.68% precision and 98.67% recall.
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TL;DR: In this paper, a simple warm restart technique for stochastic gradient descent was proposed to improve its anytime performance when training deep neural networks, which achieved state-of-the-art results on both the CIFAR-10 and CifAR-100 datasets.
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TL;DR: COVID-Net is introduced, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public, and COVIDx, an open access benchmark dataset comprising of 13,975 CXR images across 13,870 patient patient cases.
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