Classification of Coronary Stenosis using a Support Vector Machine with Automatic Parameter Tuning. Miguel-Angel Gil-Rios 1. ,. Ivan Cruz-Aceves 2.
May 10, 2023 · The main contribution is the characterization of the coronary stenosis anomaly based on the automatic selection of an efficient feature subset.
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A Support Vector Machine was employed to classify positive and negative stenosis cases, with Accuracy and the Jaccard Coefficient used as performance metrics.
In this paper, a novel strategy to perform high-dimensional feature selection using an evolutionary algorithm for the automatic classification of coronary ...
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In this paper, a novel method for the automatic classification of coronary stenosis based on a feature selection strategy driven by a hybrid evolutionary ...
"Classification of Coronary Stenosis using a Support Vector Machine with Automatic Parameter Tuning". 19th International Symposium on Medical Information ...
In this paper, a novel method for the automatic classification of coronary stenosis based on a feature selection strategy driven by a hybrid evolutionary ...
Jan 13, 2023 · High performance is aimed at the machine learning concept by adjusting the hyperparameter values of the classi er algorithms. There are many ...
An automatic deep learning-based algorithm to classify coronary stenosis lesions according to the Coronary Artery Disease Reporting and Data System (CAD-RADS)
Jul 5, 2023 · This study proposes the use of automatic segmentation of coronary arteries using U-Net, ResUNet-a, UNet++, models and classification using DenseNet201, ...
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