About: Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning
Creator	Liu, Chenglong Wang, Xiaoyang Sun, Qingfeng Liu, Chenbin Peng, Wenxian
Source	Medline; PMC
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 27 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 feature was the average weights calculated by ReliefF in n times. Features with relevance larger 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 tenfold 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.
has issue date	2020-08-19(xsd:dateTime)
bibo:doi	10.1186/s12938-020-00809-9
bibo:pmid	32814568
has license	cc-by
sha1sum (hex)	8454b19a786c7e7ca6024a87e0c9901ea897ef16
schema:url	https://doi.org/10.1186/s12938-020-00809-9
resource representing a document's title	Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning
has PubMed Central identifier	PMC7436068
has PubMed identifier	32814568
schema:publication	Biomed Eng Online
resource representing a document's body	covid:8454b19a786c7e7ca6024a87e0c9901ea897ef16#body_text
is schema:about of	named entity 'popularity' named entity 'Background' named entity 'pneumonia' named entity 'structures' named entity 'Machine learning' named entity 'Chest CT' named entity 'pneumonia' named entity 'DICOM' named entity 'transfer learning' named entity 'dry cough' named entity 'COVID' named entity 'Erlangen' named entity 'lesion' named entity 'COVID-19' named entity 'sensitivity and specificity' named entity 'nucleic acid tests' named entity 'representation learning' named entity 'sensitivity and specificity' named entity 'lung' named entity 'COVID' named entity 'ROIs' named entity 'data augmentation' named entity 'COVID-19' named entity 'COVID-19' named entity 'sensitivity, specificity' named entity 'pneumonia' named entity 'ROIs' named entity 'lungs' named entity 'feature selection' named entity 'true positive' named entity 'Transfer learning' named entity 'Machine learning' named entity 'feature selection' named entity 'COVID' named entity 'lung' named entity 'GGO' named entity 'feature selection' named entity 'pneumonia' named entity 'misdiagnosis' named entity 'prevent the spread' named entity 'deep learning' named entity 'ROIs' named entity 'YOLO' named entity 'COVID' named entity 'U-net' named entity 'bacterial pneumonia' named entity 'COVID' named entity 'histogram' named entity 'lung' named entity 'block diagram' named entity 'transfer learning' named entity 'collimation' named entity 'community acquired pneumonia' named entity 'COVID-19' named entity 'sensitivity, specificity' named entity 'pneumonia' named entity 'clinical features' named entity 'medical imaging' named entity 'ROIs' named entity 'GGO' named entity 'community acquired pneumonia' named entity 'sensitivity and specificity' named entity 'histogram' named entity 'COVID' named entity 'X-ray images' named entity 'February 5' named entity 'COVID' named entity 'histogram' named entity 'COVID-19' named entity 'lungs' named entity 'influenza virus' named entity 'validation set' named entity 'ResNet' named entity 'spiral CT' named entity 'GGO' named entity 'feature selection' named entity 'clinical diagnosis'

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