Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms
Copyright © 2020 Elsevier B.V. All rights reserved..
OBJECTIVE: This 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.
METHOD: CAD 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.
RESULTS: The 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).
CONCLUSION: This 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:144 |
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| Enthalten in: |
International journal of medical informatics - 144(2020) vom: 01. Dez., Seite 104284 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Heidari, Morteza [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 diagnosis |
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| Anmerkungen: |
Date Completed 07.12.2020 Date Revised 29.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.ijmedinf.2020.104284 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM315627204 |
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| 245 | 1 | 0 | |a Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms |
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| 500 | |a Date Completed 07.12.2020 | ||
| 500 | |a Date Revised 29.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020 Elsevier B.V. All rights reserved. | ||
| 520 | |a OBJECTIVE: This 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 | ||
| 520 | |a METHOD: CAD 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 | ||
| 520 | |a RESULTS: The 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) | ||
| 520 | |a CONCLUSION: This 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a COVID-19 diagnosis | |
| 650 | 4 | |a Computer-aided diagnosis | |
| 650 | 4 | |a Convolution neural network (CNN) | |
| 650 | 4 | |a Coronavirus | |
| 650 | 4 | |a Disease classification | |
| 650 | 4 | |a VGG16 network | |
| 700 | 1 | |a Mirniaharikandehei, Seyedehnafiseh |e verfasserin |4 aut | |
| 700 | 1 | |a Khuzani, Abolfazl Zargari |e verfasserin |4 aut | |
| 700 | 1 | |a Danala, Gopichandh |e verfasserin |4 aut | |
| 700 | 1 | |a Qiu, Yuchen |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Bin |e verfasserin |4 aut | |
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