A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images

Abstract

The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.

Keywords: chest X-ray images; data analytics; feature extraction; healthcare; image processing; pandemic.

Figure 1

Sample of the collected dataset: (a) normal images, (b) bacterial pneumonia, (c) viral pneumonia, and (d) Coronavirus-infection images.

Figure 2

The proposed model is based on the Inception-ResNet module with multiscale feature extraction and concatenation for COVID-19 classification.

Figure 3

The Inception-ResNet blocks used in our proposed model.

Figure 4

Stem block used in the proposed model.

Figure 5

The proposed deep-learning model used as feature extraction and various machine-learning classifiers used as a classification of COVID-19.

Figure 6

(a) The confusion matrix for Xception model. (b) Performance analysis based on Xception DL model. (c) Confusion matrix for CoVIRNet DL model. (d) Performance analysis based on CoVIRNet DL model.

Figure 6

(a) The confusion matrix for Xception model. (b) Performance analysis based on Xception DL model. (c) Confusion matrix for CoVIRNet DL model. (d) Performance analysis based on CoVIRNet DL model.

Figure 7

(a) The confusion matrix and performance analysis of all classes based on a proposed model with logistic regression (LR) model and (b) proposed CoVIRNet model with random forest (RF). (c) Performance metrics using logistic regression and (d) proposed CoVIRNet model with random forest.

Figure 8

(a) ROC for proposed deep-learning model with and without feature extraction compared with an existing deep-learning model. (b) Precision–recall curves for proposed model compared with existing state-of-the-art deep-learning models.

Figure 9

(a) ROC for proposed deep-learning model, with feature extraction using various machine-learning models. (b) Precision–recall curves for the proposed model, with feature extraction using various machine-learning models.

Figure 10

(a) The proposed model’s feature importance, using the random forest for four classes; (b) the feature importance impact, using SHAP library, shows high and low importance.

Figure 11

The visualization of some predicted samples based on our proposed model, using the RISE library.

Figure 12

Deployed proposed deep-learning model for assessment and detection of COVID-19 in clinical application.