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Computer-Aided Diagnosis (CAD) to Detect Abnormality on CT Image of Liver
W. S. Wahyuni
,
P. Prajitno
, D. S. Soejoko
Department of Physics
Research output
:
Contribution to journal
›
Conference article
›
peer-review
1
Citation (Scopus)
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Dive into the research topics of 'Computer-Aided Diagnosis (CAD) to Detect Abnormality on CT Image of Liver'. Together they form a unique fingerprint.
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Keyphrases
Abdomen
50%
Active Contour
50%
Artificial Neural Network
100%
Benign or Malignant
50%
Benign Tumor
50%
Bogor
50%
Cancer Patients
50%
Computed Tomography Scanner
50%
Computer-aided Diagnosis (CADx)
100%
Contour-based Segmentation
50%
Contrast Difference
50%
CT Scan Images
50%
Data Collection Methods
50%
Feature Extraction Methods
50%
Fibrosis
50%
Gray-level Co-occurrence
50%
Image Data
50%
Liver
50%
Liver Abnormality
100%
Liver Cancer
100%
Liver CT Image
100%
Liver Patient
50%
Low Contrast
100%
Machine Learning
50%
Malignant Tumor
50%
Normal Liver
100%
Overall Error
50%
Regional Public Hospital
50%
Segmentation Method
50%
Small Mass
50%
Texture Analysis
50%
Computer Science
Active Contour
50%
Aided Diagnosis
100%
Artificial Neural Network
100%
Cancer Patient
50%
Data Collection Technique
50%
Extraction Process
50%
Feature Extraction
50%
Learning System
50%
Machine Learning
50%
Occurrence Matrix
50%
Secondary Data
50%
Segmentation Method
50%
Texture Analysis
50%
Engineering
Active Contour
50%
Artificial Neural Network
100%
Based Segmentation Method
50%
Computer Aided Diagnosis
100%
Cooccurrence Matrix
50%
Extraction Process
50%
Feature Extraction
50%
Grey Level
50%
Image Data
50%
Learning System
50%
Scan Image
50%
Texture Analysis
50%
Earth and Planetary Sciences
Artificial Neural Network
100%
Machine Learning
50%
Pattern Recognition
50%
Tomography
50%
Material Science
Tomography
100%
Tumor
100%
Chemical Engineering
Learning System
50%
Neural Network
100%