tetano
Editor, Senior Moderator
Int J Med Inform
. 2020 Sep 23;144:104284.
doi: 10.1016/j.ijmedinf.2020.104284. Online ahead of print.
Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms
Morteza Heidari[SUP] 1 [/SUP], Seyedehnafiseh Mirniaharikandehei[SUP] 2 [/SUP], Abolfazl Zargari Khuzani[SUP] 3 [/SUP], Gopichandh Danala[SUP] 2 [/SUP], Yuchen Qiu[SUP] 2 [/SUP], Bin Zheng[SUP] 2 [/SUP]
Affiliations
Abstract
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.
Keywords: COVID-19 diagnosis; Computer-aided diagnosis; Convolution neural network (CNN); Coronavirus; Disease classification; VGG16 network.
. 2020 Sep 23;144:104284.
doi: 10.1016/j.ijmedinf.2020.104284. Online ahead of print.
Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms
Morteza Heidari[SUP] 1 [/SUP], Seyedehnafiseh Mirniaharikandehei[SUP] 2 [/SUP], Abolfazl Zargari Khuzani[SUP] 3 [/SUP], Gopichandh Danala[SUP] 2 [/SUP], Yuchen Qiu[SUP] 2 [/SUP], Bin Zheng[SUP] 2 [/SUP]
Affiliations
- PMID: 32992136
- PMCID: PMC7510591
- DOI: 10.1016/j.ijmedinf.2020.104284
Abstract
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.
Keywords: COVID-19 diagnosis; Computer-aided diagnosis; Convolution neural network (CNN); Coronavirus; Disease classification; VGG16 network.