Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays

Wang, Zheng; Xiao, Ying; Li, Yong; Zhang, Jie; Lu, Fanggen; Hou, Muzhou; Liu, Xiaowei

doi:10.1016/j.patcog.2020.107613

Cited by 150 publications

(117 citation statements)

References 32 publications

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“…Most of these papers used off-the-shelf networks, including ResNet-18 or ResNet-50 16,17,20,26,29,32,37 , DenseNet-121 27,28,31,32,34 , VGG-16 or VGG-19 19,33,35 , Inception 21,38 and EfficientNet 30,39 , with three considering custom architectures 18,25,36 and three using hand-engineered features [22][23][24] . Most papers classified images into the three classes, that is, COVID-19, non-COVID-19 pneumonia and normal 16,19,21,23,25,26,28,30,[32][33][34][35][36][37] , while two considered an extra class by dividing non-COVID-19 pneumonia into viral and bacterial pneumonia 17,29 . ResNet and DenseNet architectures showed better performance than the others, with accuracies ranging from 0.88 to 0.99.…”

Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

“…For the 33 papers that used private datasets, the COVID-19 diagnosis was due to either positive RT-PCR or antibody tests for 24/33 and have a low risk of bias. The other papers have a high (7/33) or unclear (2/33) risk of bias due to inconsistent diagnosis of COVID-19 40,82 , unclear definition of a control group 63,65 , ground truths being assigned using the images themselves 26,54,60,71 , using an unestablished reference to define outcome 74 or by combining public and private datasets 41,66,83 .…”

Section: Risks Of Biasmentioning

confidence: 99%

“…We see that 20/32 papers have a reasonable balance between classes (with exceptions being refs. 17,24,26,30,31,33,36,37,40,51,61,62 . However, the majority of datasets were quite small, with 19/32 papers using fewer than 2,000 datapoints for development (with exceptions being refs.…”

Section: Model Evaluation Inmentioning

confidence: 99%

“…However, the majority of datasets were quite small, with 19/32 papers using fewer than 2,000 datapoints for development (with exceptions being refs. 17,18,26,27,30,31,33,36,41,48,53,57,81 ). Only seven papers used both a dataset with more than 2,000 datapoints that was balanced for COVID-19 positive and the other classes 27,41,48,53,54,57,81 .…”

Section: Model Evaluation Inmentioning

confidence: 99%

See 3 more Smart Citations

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

et al. 2021

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Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.

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Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

Section: Risks Of Biasmentioning

confidence: 99%

Section: Model Evaluation Inmentioning

confidence: 99%

Section: Model Evaluation Inmentioning

confidence: 99%

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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

et al. 2021

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“…With the current spread rate of COVID-19, this is not acceptable. Further, the limited number of test kits exacerbates the situation [1] , [2] , [3] . Recent studies also show that the RT-PCR suffers from low sensitivity and accuracy, often requiring repeated entries [4] , [5] .…”

Section: Introductionmentioning

confidence: 99%

Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images

Chen

Yao

Zhou

et al. 2021

Pattern Recognition

154

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Advancement of deep learning in pneumonia/Covid‐19 classification and localization: A systematic review with qualitative and quantitative analysis

Shah

2022

Chronic Diseases and Translational Medicine

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Around 450 million people are affected by pneumonia every year, which results in 2.5 million deaths. Coronavirus disease 2019 (Covid‐19) has also affected 181 million people, which led to 3.92 million casualties. The chances of death in both of these diseases can be significantly reduced if they are diagnosed early. However, the current methods of diagnosing pneumonia (complaints + chest X‐ray) and Covid‐19 (real‐time polymerase chain reaction) require the presence of expert radiologists and time, respectively. With the help of deep learning models, pneumonia and Covid‐19 can be detected instantly from chest X‐rays or computerized tomography (CT) scans. The process of diagnosing pneumonia/Covid‐19 can become faster and more widespread. In this paper, we aimed to elicit, explain, and evaluate qualitatively and quantitatively all advancements in deep learning methods aimed at detecting community‐acquired pneumonia, viral pneumonia, and Covid‐19 from images of chest X‐rays and CT scans. Being a systematic review, the focus of this paper lies in explaining various deep learning model architectures, which have either been modified or created from scratch for the task at hand. For each model, this paper answers the question of why the model is designed the way it is, the challenges that a particular model overcomes, and the tradeoffs that come with modifying a model to the required specifications. A grouped quantitative analysis of all models described in the paper is also provided to quantify the effectiveness of different models with a similar goal. Some tradeoffs cannot be quantified and, hence, they are mentioned explicitly in the qualitative analysis, which is done throughout the paper. By compiling and analyzing a large quantum of research details in one place with all the data sets, model architectures, and results, we aimed to provide a one‐stop solution to beginners and current researchers interested in this field.

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Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays

Cited by 150 publications

References 32 publications

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images

Advancement of deep learning in pneumonia/Covid‐19 classification and localization: A systematic review with qualitative and quantitative analysis

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