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Texture-based classification of hysteroscopy images of the endometrium.
M. S. Neofytou
, M. S. Pattichis
, C. S. Pattichis
, V. Tanos
, E. C. Kyriacou
, D. D. Koutsouris
Medical School
University of Cyprus
Research output
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Contribution to journal
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Article
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peer-review
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Keyphrases
Region of Interest
100%
Hysteroscopy
100%
Endometrium
100%
Texture-based Classification
100%
Abnormal Region
66%
Texture Features
50%
Gray Difference
33%
Difference Variables
33%
Statistical Features
33%
Early Detection
16%
High Contrast
16%
Texture Analysis
16%
Wilcoxon Rank Sum Test
16%
High Entropy
16%
Gray-scale Median
16%
Grayscale
16%
Dependence Matrix
16%
Entropy Value
16%
Contrast Value
16%
Classification Score
16%
Gynecological Cancer
16%
Spatial Grey Level Dependence
16%
SVM Neural Network
16%
Neural Network Classifier
16%
SVM Model
16%
PNN Neural Network
16%
Agricultural and Biological Sciences
Hysteroscopy
100%
Endometrium
100%
Neural Network
33%