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Classification of fetal heart ultrasound images for the detection of chd
T. V. Sushma
*
, N. Sriraam
,
P. Megha Arakeri
, S. Suresh
*
Corresponding author for this work
School of Computer Engineering
Research output
:
Chapter in Book/Report/Conference proceeding
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Chapter
5
Citations (Scopus)
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INIS
classification
100%
detection
100%
images
100%
congenital diseases
100%
heart disease
66%
validation
33%
growth
33%
sensitivity
33%
mortality
33%
specificity
33%
screening
33%
accuracy
33%
monitoring
33%
simulation
33%
vectors
33%
defects
33%
infants
33%
ultrasonics
33%
fetuses
33%
kernels
33%
accounting
33%
Computer Science
Support Vector Machine
100%
Ultrasound Image
100%
Support Vector Machine
100%
Early Detection
100%
Statistical Feature
100%
Imaging Modality
100%
Gaussian Kernel
100%
Biochemistry, Genetics and Molecular Biology
Gaussian Distribution
100%
Ultrasound
33%
Support Vector Machine
33%
Kernel Method
33%
Prenatal Screening
33%
Infant Mortality
33%
Pharmacology, Toxicology and Pharmaceutical Science
Congenital Heart Disease
100%
Cross-Validation
50%
Infant Mortality
50%
Congenital Disorder
50%
Physics
Gaussian Distribution
100%
Ultrasonics
25%