Machine Learning Model Based on Radiomic Features for Differentiation between COVID-19 and Pneumonia on Chest X-ray
DC Field | Value | Language |
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dc.contributor.author | Kim, Young Jae | - |
dc.date.accessioned | 2022-10-12T06:40:10Z | - |
dc.date.available | 2022-10-12T06:40:10Z | - |
dc.date.created | 2022-09-22 | - |
dc.date.issued | 2022-09 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/85655 | - |
dc.description.abstract | Machine learning approaches are employed to analyze differences in real-time reverse transcription polymerase chain reaction scans to differentiate between COVID-19 and pneumonia. However, these methods suffer from large training data requirements, unreliable images, and uncertain clinical diagnosis. Thus, in this paper, we used a machine learning model to differentiate between COVID-19 and pneumonia via radiomic features using a bias-minimized dataset of chest X-ray scans. We used logistic regression (LR), naive Bayes (NB), support vector machine (SVM), k-nearest neighbor (KNN), bagging, random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM) to differentiate between COVID-19 and pneumonia based on training data. Further, we used a grid search to determine optimal hyperparameters for each machine learning model and 5-fold cross-validation to prevent overfitting. The identification performances of COVID-19 and pneumonia were compared with separately constructed test data for four machine learning models trained using the maximum probability, contrast, and difference variance of the gray level co-occurrence matrix (GLCM), and the skewness as input variables. The LGBM and bagging model showed the highest and lowest performances; the GLCM difference variance showed a high overall effect in all models. Thus, we confirmed that the radiomic features in chest X-rays can be used as indicators to differentiate between COVID-19 and pneumonia using machine learning. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | SENSORS | - |
dc.title | Machine Learning Model Based on Radiomic Features for Differentiation between COVID-19 and Pneumonia on Chest X-ray | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000851693700001 | - |
dc.identifier.doi | 10.3390/s22176709 | - |
dc.identifier.bibliographicCitation | SENSORS, v.22, no.17 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85137562260 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 22 | - |
dc.citation.number | 17 | - |
dc.contributor.affiliatedAuthor | Kim, Young Jae | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | pneumonia | - |
dc.subject.keywordAuthor | radiomic feature | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | chest X-ray | - |
dc.subject.keywordPlus | CT | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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