Application of neural networks in detection of abnormal brain perfusion regions

Neurocomputing

Volumn 122, Issue , 2013, Pages 33-42

  Hachaj, Tomasz ^a   Ogiela, Marek R ^b  

^a PEDAGOGICAL UNIVERSITY   (Poland)

^b AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Poland)

Author keywords

Abnormality detection; Classification; Dynamic perfusion computed tomography; Image Professing; Multilayer perceptron; Self organizing maps

Indexed keywords

ABNORMALITY DETECTION; COMPUTED TOMOGRAPHY PERFUSIONS (CTP); IMAGE PROFESSING; MULTI LAYER PERCEPTRON; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; PERFUSION ABNORMALITIES; PROCESSING PROCEDURES; TIME SERIES CLASSIFICATIONS;

CARDIOVASCULAR SYSTEM; CLASSIFICATION (OF INFORMATION); CONFORMAL MAPPING; IMAGE PROCESSING; NEURAL NETWORKS; SELF ORGANIZING MAPS;

COMPUTERIZED TOMOGRAPHY;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BRAIN ASYMMETRY; BRAIN BLOOD FLOW; BRAIN BLOOD VOLUME; BRAIN INFARCTION; BRAIN ISCHEMIA; BRAIN PERFUSION; CEREBROVASCULAR ACCIDENT; CLINICAL ARTICLE; COMPUTER ASSISTED TOMOGRAPHY; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE SEVERITY; FEMALE; HUMAN; IMAGE PROCESSING; INTERMETHOD COMPARISON; MALE; PERCEPTRON; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SELF ORGANIZING MAP; SENSITIVITY AND SPECIFICITY;

EID: 84884210619     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.04.030     Document Type: Article

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