Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images

Abstract

X-ray units have become one of the most advantageous candidates for triaging the new Coronavirus disease COVID-19 infected patients thanks to its relatively low radiation dose, ease of access, practical, reduced prices, and quick imaging process. This research intended to develop a reliable convolutional-neural-network (CNN) model for the classification of COVID-19 from chest X-ray views. Moreover, it is aimed to prevent bias issues due to the database. Transfer learning-based CNN model was developed by using a sum of 1,218 chest X-ray images (CXIs) consisting of 368 COVID-19 pneumonia and 850 other pneumonia cases by pre-trained architectures, including DenseNet-201, ResNet-18, and SqueezeNet. The chest X-ray images were acquired from publicly available databases, and each individual image was carefully selected to prevent any bias problem. A stratified 5-fold cross-validation approach was utilized with a ratio of 90% for training and 10% for the testing (unseen folds), in which 20% of training data was used as a validation set to prevent overfitting problems. The binary classification performances of the proposed CNN models were evaluated by the testing data. The activation mapping approach was implemented to improve the causality and visuality of the radiograph. The outcomes demonstrated that the proposed CNN model built on DenseNet-201 architecture outperformed amongst the others with the highest accuracy, precision, recall, and F1-scores of 94.96%, 89.74%, 94.59%, and 92.11%, respectively. The results indicated that the reliable diagnosis of COVID-19 pneumonia from CXIs based on the CNN model opens the door to accelerate triage, save critical time, and prioritize resources besides assisting the radiologists.

Keywords: Chest X-ray images; Convolutional Neural Network (CNN); Corona Virus (COVID-19); Deep learning.

Fig. 1

Representative chest X-ray images for other pneumonia class (upper row) and COVID-19 pneumonia class (bottom row).

Fig. 2

The schematic illustration of the developed CNN model.

Scheme 1

Representative flowchart of proposed CNN approach.

Fig. 3

Schematic illustration of 5-fold cross-validation approach.

Fig. 4

The comparison of performance metrics for DenseNet-201, ResNet-18, SqueezeNet architectures obtained in Fold-1 for the CNN model.

Fig. 5

The overlapped confusion matrices of validation data for (A) DenseNet-201, (B) ResNet-18, and (C) SqueezeNet architectures.

Fig. 6

The calculated confusion matrices of testing data for (A) DenseNet-201, (B) ResNet-18, and (C) SqueezeNet architectures.

Fig. 7

The representative illustration of original chest X-ray images (left-side) and Grad-CAM activation mapping (right-side) of other pneumonia and COVID-19 pneumonia cases.