Machine-learning-based classification of Glioblastoma using MRI-based radiomic features.
Ge CuiJiwoong Jason JeongYang LeiTonghe WangTian LiuWalter J. CurranHui MaoXiaofeng YangPublished in: Medical Imaging: Computer-Aided Diagnosis (2019)
Keyphrases
- machine learning
- feature vectors
- feature set
- classification accuracy
- feature selection
- feature construction
- classification models
- classification method
- text classification
- machine learning approaches
- feature extraction
- feature space
- magnetic resonance imaging
- feature generation
- extracted features
- pattern recognition
- supervised machine learning
- machine learning methods
- machine learning models
- classification process
- feature analysis
- decision trees
- feature subset
- supervised learning
- discriminative features
- feature representation
- features extraction
- benchmark datasets
- learning algorithm
- high dimensionality
- feature weights
- support vector machine
- unsupervised learning
- model selection
- feature reduction
- active learning
- pattern classification
- image features
- feature ranking
- classification scheme
- class labels
- information extraction
- feature values
- eeg signals
- text mining
- supervised classification
- feature selection algorithms
- machine learning algorithms
- false positives
- natural language processing
- image classification
- multi class
- training samples
- gabor filters
- extracting features
- text categorization
- knn
- high resolution
- training set