Evidence map›Paper›PMID 32992136›Full record

ArticleInternational journal of medical informatics2020

Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms.

Morteza Heidari, Seyedehnafiseh Mirniaharikandehei, Abolfazl Zargari Khuzani, Gopichandh Danala, Yuchen Qiu, Bin Zheng

Abstract read
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In one paragraph

Article in International journal of medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
122citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

122 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  4. Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021
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62 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Morteza HeidariSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA. Electronic address: Morteza.heidari@ou.edu.
Seyedehnafiseh MirniaharikandeheiSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.
Abolfazl Zargari KhuzaniDepartment of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA.
Gopichandh DanalaSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.
Yuchen QiuSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.
Bin ZhengSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.

Funding

Increasing Cancer Detection Yield Using Breast MRI Screening ModalityR01CA197150 · NCI · UNIVERSITY OF OKLAHOMA · PI HOLLINGSWORTH, ALAN B., LIU, HONG · 2015 to 2019
$2.5M
NCI NIH HHS R01 CA197150
6 · The paper itself

Abstract

objectiveThis study aims to develop and test a new computer-aided diagnosis (CAD) scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia.

methodCAD scheme first applies two image preprocessing steps to remove the majority of diaphragm regions, process the original image using a histogram equalization algorithm, and a bilateral low-pass filter. Then, the original image and two filtered images are used to form a pseudo color image. This image is fed into three input channels of a transfer learning-based convolutional neural network (CNN) model to classify chest X-ray images into 3 classes of COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) cases. To build and test the CNN model, a publicly available dataset involving 8474 chest X-ray images is used, which includes 415, 5179 and 2,880 cases in three classes, respectively. Dataset is randomly divided into 3 subsets namely, training, validation, and testing with respect to the same frequency of cases in each class to train and test the CNN model.

resultsThe CNN-based CAD scheme yields an overall accuracy of 94.5 % (2404/2544) with a 95 % confidence interval of [0.93,0.96] in classifying 3 classes. CAD also yields 98.4 % sensitivity (124/126) and 98.0 % specificity (2371/2418) in classifying cases with and without COVID-19 infection. However, without using two preprocessing steps, CAD yields a lower classification accuracy of 88.0 % (2239/2544).

conclusionThis study demonstrates that adding two image preprocessing steps and generating a pseudo color image plays an important role in developing a deep learning CAD scheme of chest X-ray images to improve accuracy in detecting COVID-19 infected pneumonia.

Indexed as

AlgorithmsNeural Networks, ComputerCOVID-19Deep LearningDiagnosis, Computer-AssistedHumansRadiography, ThoracicSARS-CoV-2Tomography, X-Ray ComputedComputer-aided diagnosisConvolution neural network (CNN)CoronavirusCOVID-19 diagnosisDisease classificationVGG16 network

Identifiers

PMID32992136
PMCPMC7510591

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.