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

Autor: Gopichandh Danala, Bin Zheng, Seyedehnafiseh Mirniaharikandehei, Abolfazl Zargari Khuzani, Morteza Heidari, Yuchen Qiu
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
Computer Science - Machine Learning
COVID-19 diagnosis
020205 medical informatics
Computer science
Health Informatics
CAD
02 engineering and technology
Convolutional neural network
Article
Machine Learning (cs.LG)
03 medical and health sciences
0302 clinical medicine
Deep Learning
FOS: Electrical engineering
electronic engineering
information engineering
0202 electrical engineering
electronic engineering
information engineering
Preprocessor
Humans
030212 general & internal medicine
Diagnosis
Computer-Assisted
Histogram equalization
business.industry
Color image
SARS-CoV-2
Deep learning
Image and Video Processing (eess.IV)
COVID-19
Electrical Engineering and Systems Science - Image and Video Processing
Computer-aided diagnosis
Disease classification
3. Good health
Coronavirus
Convolution neural network (CNN)
Radiography
Thoracic
VGG16 network
Artificial intelligence
Neural Networks
Computer
business
Transfer of learning
Tomography
X-Ray Computed
Algorithm
Algorithms
Zdroj: International Journal of Medical Informatics
ISSN: 1872-8243
Popis: Highlights • Radiographic chest images can be used to more accurately detect COVID-19 and assess disease severity. Among different imaging modalities, chest X-ray radiography has advantages of low cost, low radiation dose, wide accessibility and easy-to-operate in general or community hospitals. • This study aims to develop and test a new deep learning model of chest X-ray images to detect COVID-19 induced pneumonia. For this purpose, we assembled a relatively large chest X-ray image dataset involving 8474 cases, which are divided into three groups of COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) cases. • After applying a preprocessing algorithm to detect and remove diaphragm regions depicting on images, a histogram equalization algorithm and a bilateral filter are applied to process the original images to generate two sets of filtered images. Then, the original image plus these two filtered images are used as inputs of three channels of the CNN deep learning model, which increase learning information of the model. • In order to fully take advantages of the pre-optimized CNN models, this study uses a transfer learning method to build a new model to detect and classify COVID-19 infected pneumonia. A VGG16 based CNN model was originally trained using ImageNet and fine-tuned using chest X-ray images in this study. • Testing on a subset of 2544 cases, the CNN model yields 94.5% accuracy in classifying three subsets of cases and 98.1% accuracy in detecting COVID-19 infected pneumonia cases, which are significantly higher than the model directly trained using the original images without applying two image preprocessing steps to remove diaphragm and generate two filtered images.
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.
Databáze: OpenAIRE
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