Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

Abstract

Background: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray level co-occurrence matrix (GLCM) features, 15 gray level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each features was the average weights calculated by ReliefF in n times. Features with relevance lager than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using 10-fold cross-validation. Results and Conclusions: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine

“…Abraham et al [ 26 ] combined multiple CNN-extracted features with a correlation-based feature selection (CFS) technique and a Bayesian classifier for COVID-19 prediction. Liu et al [ 27 ] developed and validated a complete machine learning framework for chest CT images to distinguish COVID-19 from global pneumonia (GP). Tuncer et al [ 28 ] proposed a novel intelligent computer vision method for automatic detection of the COVID-19 virus and conducted 10-fold cross-validation based on the SVM classifier, which showed a classification accuracy of 100.0%.…”

A Practical Deep Learning Model in Differentiating Pneumonia-Type Lung Carcinoma from Pneumonia on CT Images: ResNet Added with Attention Mechanism

“…For example, a convolutional neural network was used to distinguish between pneumonia patients and healthy controls [30]. Besides, novel coronavirus pneumonia was differentiated from general pneumonia using the convolutional neural network [31]. Moreover, a variety of deep learning feature embedding methods were used to improve the recognition accuracy of COVID-19 pneumonia [32], screen diseased frames from pneumonia images [33], and screen patients with pneumonia [34].…”

Chest X‐ray lung imaging features in pediatric COVID‐19 and comparison with viral lower respiratory infections in young children

“…In studies of adults with COVID‐19, lung imaging quantification has been successfully implemented to predict adverse outcomes and severe complications 13–16 . We previously described CXR‐based methods to quantify lung disease severity in pediatric viral LRTIs 19–23 .…”

“…We found that 64% of lung CT images in PCR‐confirmed pediatric COVID‐19 cases had abnormalities, primarily characterized by focal ground‐glass opacities (GGO) and consolidations 2 . In adult COVID‐19 cases, CT scan‐based algorithms have recently been developed for lung disease quantification and prediction of life‐threatening complications 13–16 . However, chest CT‐based risk prediction approaches cannot be readily applied to infants and children due to concerns about sedation requirements, radiation exposure, and costs 17,18 .…”