Comput. Biol. Med. - Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI.

Tópicos

{ featur(3375) classif(2383) classifi(1994) }
{ imag(2675) segment(2577) method(1081) }
{ analysi(2126) use(1163) compon(1037) }
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{ gene(2352) biolog(1181) express(1162) }
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{ method(1969) cluster(1462) data(1082) }

Resumo

Dementia is a growing concern due to the aging process of the western societies. Non-invasive detection is therefore a high priority research endeavor. In this paper we report results of classification systems applied to the feature vectors obtained by a feature extraction method computed on structural magnetic resonance imaging (sMRI) volumes for the detection of two neurological disorders with cognitive impairment: myotonic dystrophy of type 1 (MD1) and Alzheimer disease (AD). The feature extraction process is based on the voxel clusters detected by voxel-based morphometry (VBM) analysis of sMRI upon a set of patient and control subjects. This feature extraction process is specific for each kind of disease and is grounded on the findings obtained by medical experts. The 10-fold cross-validation results of several statistical and neural network based classification algorithms trained and tested on these features show high specificity and moderate sensitivity of the classifiers, suggesting that the approach is better suited for rejecting than for detecting early stages of the diseases.

Resumo Limpo

dementia grow concern due age process western societi noninvas detect therefor high prioriti research endeavor paper report result classif system appli featur vector obtain featur extract method comput structur magnet reson imag smri volum detect two neurolog disord cognit impair myoton dystrophi type md alzheim diseas ad featur extract process base voxel cluster detect voxelbas morphometri vbm analysi smri upon set patient control subject featur extract process specif kind diseas ground find obtain medic expert fold crossvalid result sever statist neural network base classif algorithm train test featur show high specif moder sensit classifi suggest approach better suit reject detect earli stage diseas

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