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Online ISSN: 1945-4589
Research Paper
|
Volume 13, Issue 9
|
pp. 12833–12848
Predicting intraventricular hemorrhage growth with a machine learning-based, radiomics-clinical model
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Figure 6
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100%
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Figure 6.
(
A
) Regions of interest were manually segmented. (
B
) A total of 396 features were extracted. (
C
) Features were selected using LASSO method. (
D
) Rad-score was calculated. (
E
) Predicting model was developed using support vector machine.
