Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning.
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.Me...
| Publicado en: | BioMedical Engineering OnLine Vol. 19; no. 1 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
8/19/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145256711&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145256711 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 8/19/2020 vid: 19 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 145256711 145256711 NLM32814568 10.1186/s12938-020-00809-9 NLM32814568 145256711 ppct: 1 formats: tig: atl: Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning. aug: au: Liu, Chenglong Wang, Xiaoyang Liu, Chenbin Sun, Qingfeng Peng, Wenxian affil: School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, 200093, Shanghai, China sug: subj: Pneumonia Diagnosis Image Processing, Computer Assisted Pneumonia, Viral Complications COVID-19 Complications Pneumonia Complications Female Male Tomography, X-Ray Computed Disease Outbreaks Impact of Events Scale Questionnaires Female Male ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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