2016
Artificial Neural Networks for First-episode Schizophrenia Classification Based on MRI Data
VYŠKOVSKÝ, Roman, Eva JANOUŠOVÁ a Daniel SCHWARZZákladní údaje
Originální název
Artificial Neural Networks for First-episode Schizophrenia Classification Based on MRI Data
Autoři
VYŠKOVSKÝ, Roman, Eva JANOUŠOVÁ a Daniel SCHWARZ
Vydání
ISCAMI 2016 Book of Abstracts, 2016
Další údaje
Jazyk
angličtina
Typ výsledku
Konferenční abstrakt
Obor
20200 2.2 Electrical engineering, Electronic engineering, Information engineering
Stát vydavatele
Česká republika
Utajení
není předmětem státního či obchodního tajemství
Organizační jednotka
Lékařská fakulta
Klíčová slova česky
umělé neuronové sítě; diagnostika schizofrenie
Klíčová slova anglicky
artificial neural networks; schizophrenia diagnostics
Příznaky
Mezinárodní význam
Změněno: 14. 7. 2016 08:41, RNDr. Roman Vyškovský, Ph.D.
Anotace
V originále
Diagnostics of schizophrenia is based on self-report, manifestation of symptoms, psychiatrist's experience and interview with patient's family. No objective diagnostic tool is available and therefore there is a demand for computer-aided tools that would help in recognition of schizophrenia in the first episode. Early detection could accelerate deployment of antipsychotics and improve prognosis of patients suffering from this severe mental disorder. In this study, we applied several types of artificial neural networks - multilayer perceptron, radial basis function network (RBF) and learning vector quantization network - and explored the influence of their parameters such as number of input or hidden neurons on the classification performance. Our dataset consisted of 104 T1-weighted magnetic resonance images (52 patients and 52 control subjects) processed with the use of optimized voxel based morphometry. We selected only the most significant voxels as the features based on two-sample t-test. To ensure a methodologically appropriate framework, both the feature selection step and the classification step were validated using leave-one-out cross-validation. The highest accuracy among the proposed classifiers was 76% and it was achieved by RBF network.