A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD)

Physics in Medicine and Biology

Volumn 54, Issue 18, 2009, Pages

  Suzuki, Kenji ^a  

^a UNIVERSITY OF CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE AREA; ARTIFICIAL NEURAL NETWORK; CONVENTIONAL FILTERS; CT IMAGE; DETECTION OF LUNG NODULES; FALSE POSITIVE; FILTER-BASED; GROUND-GLASS OPACITY; HESSIAN MATRICES; INHOMOGENEITIES; LUNG CANCER; LUNG NODULE; MEDICAL IMAGE ANALYSIS; SENSITIVITY AND SPECIFICITY; SIMPLE MODEL; SOLID SPHERES; TRUE POSITIVE;

BACKPROPAGATION; BIOLOGICAL ORGANS; COMPUTER AIDED ANALYSIS; COMPUTER AIDED DESIGN; COMPUTERIZED TOMOGRAPHY; IMAGE ANALYSIS; NEURAL NETWORKS;

COMPUTER AIDED DIAGNOSIS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; CONTROLLED STUDY; DATA BASE; FILTER; IMAGE ANALYSIS; LUNG CANCER; LUNG NODULE; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; TRAINING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; LUNG NEOPLASMS; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SOLITARY PULMONARY NODULE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 71049155885     PISSN: 00319155     EISSN: 13616560     Source Type: Journal    
DOI: 10.1088/0031-9155/54/18/S03     Document Type: Article

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