Random forest based lung nodule classification aided by clustering

Computerized Medical Imaging and Graphics

Volumn 34, Issue 7, 2010, Pages 535-542

  Lee, S L A ^a   Kouzani, A Z ^a   Hu, E J ^b  

^a DEAKIN UNIVERSITY   (Australia)

^b UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

Classification aided by clustering; Ensemble classification; Lung images; Pulmonary nodules

Indexed keywords

AUTOMATED LUNG NODULE DETECTION; CLASSIFICATION AIDED BY CLUSTERING; ENSEMBLE CLASSIFICATION; EXISTING METHOD; EXISTING SYSTEMS; LUNG ABNORMALITIES; LUNG IMAGES; LUNG NODULE; LUNG NODULE DETECTION; MULTIPLE CLASSIFIERS; PULMONARY NODULES; RANDOM FOREST ALGORITHM; RANDOM FORESTS; RECEIVER OPERATING CHARACTERISTICS; TEMPLATE-BASED;

BIOLOGICAL ORGANS; CLASSIFIERS; DECISION TREES; LEARNING ALGORITHMS;

COMPUTERIZED TOMOGRAPHY;

ALGORITHM; ARTICLE; AUTOMATION; COMPUTER ASSISTED TOMOGRAPHY; DISEASE CLASSIFICATION; EXPERIMENTAL STUDY; HUMAN; IMAGE ANALYSIS; LUNG NODULE; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY;

CLUSTER ANALYSIS; HUMANS; LUNG; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; SENSITIVITY AND SPECIFICITY; SOLITARY PULMONARY NODULE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 77955659952     PISSN: 08956111     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2010.03.006     Document Type: Article

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