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Kim, Jong-hun;Park, Hyunjin
- Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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- 2022.10a
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- pp.289-290
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- 2022
Arteriosclerosis is a disease in which the carotid vessel wall becomes thick, and it is important to monitor the thickness of the vessel wall for diagnosis. In this study, we propose a model for extracting 324 radiomics features from carotid MRI images and diagnosing arteriosclerosis using machine learning techniques. We learned a total of four classification models: logistic regression, support vector machine, random forest, and XGBoost through radiomics features. XGBoost model, which showed the highest performance in 5-fold cross-validation, shows the results of accuracy 0.9023, sensitivity 0.9517, specificity 0.8035, AUC 0.8776.
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Na, In-ye;Park, Hyunjin
- Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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- 2022.10a
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- pp.294-296
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- 2022
Atherosclerosis is a chronic vascular inflammatory disease in which plaque builds up in the arteries and impairs blood flow. This can lead to heart disease and stroke. Since most people do not have any symptoms until the artery is severely narrowed, early detection of atherosclerosis is critical. In this paper, in order to effectively detect atherosclerotic lesions in tube-shaped blood vessels, polar conversion is applied to MRI images based on the vessel center. We then propose a SE-LSTMNet model using continuous signal information for each angle of a polar coordinate image. The trained model showed classification performance of 0.9194 accuracy, 0.9370 sensitivity, 0.8796 specificity, 0.8700 F1 score, and 0.9719 AUC on the validation data.
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