COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm

Abstract

As a pandemic, the primary evaluation tool for coronavirus (COVID-19) still has serious flaws. To improve the existing situation, all facilities and tools available in this field should be used to combat the pandemic. Reverse transcription polymerase chain reaction is used to evaluate whether or not a person has this virus, but it cannot establish the severity of the illness. In this paper, we propose a simple, reliable, and automatic system to diagnose the severity of COVID-19 from the CT scans into three stages: mild, moderate, and severe, based on the simple segmentation method and three types of features extracted from the CT images, which are ratio of infection, statistical texture features (mean, standard deviation, skewness, and kurtosis), GLCM and GLRLM texture features. Four machine learning techniques (decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), and Naïve Bayes) are used to classify scans. 1801 scans are divided into four stages based on the CT findings in the scans and the description file found with the datasets. Our proposed model divides into four steps: preprocessing, feature extraction, classification, and performance evaluation. Four machine learning algorithms are used in the classification step: SVM, KNN, DT, and Naive Bayes. By SVM method, the proposed model achieves 99.12%, 98.24%, 98.73%, and 99.9% accuracy for COVID-19 infection segmentation at the normal, mild, moderate, and severe stages, respectively. The area under the curve of the model is 0.99. Finally, our proposed model achieves better performance than state-of-art models. This will help the doctors know the stage of the infection and thus shorten the time and give the appropriate dose of treatment for this stage.

Keywords: COVID-19; CT scans; Decision tree; KNN; Mild stage; Moderate stage; Naïve Bayes; SVM; Segmentation; Severe stage; The severity of infection.

Fig. 1

Our proposed methodology

Fig. 2

Samples from the removing step (upper and lower parts are removed; full regions remain) and Samples from the cropping step

Fig. 3

Ratio of Infection extraced feature step

Fig. 4

Samples from mild, moderate, and severe stages and the steps to extract features

Fig. 5

Classification steps

Fig. 6

The resulting of selecting the best number of folds for KNN,SVM, and Naïve Bayes in mild stage. The best value is 8

Fig. 7

The resulting of selecting the best number of folds for DT, KNN,SVM, and Naïve Bayes in moderate stage. The best value is 8

Fig. 8

The resulting of selecting the best number of folds for DT, KNN, SVM, and Naïve Bayes in sever stage. The best value is 8

Fig. 9

The confusion matrices for ML models with our proposed feature extraction method

Fig. 10

ROC Curves for ML models with our proposed feature extraction method

Fig. 11

Performance metrics for ML models with our proposed model

Fig. 12

The average accuracy of four stages for four models based on each feature