Artificial Neural Networks for First-episode Schizophrenia Classification Based on MRI Data

VYŠKOVSKÝ, Roman, Eva JANOUŠOVÁ a Daniel SCHWARZ. Artificial Neural Networks for First-episode Schizophrenia Classification Based on MRI Data. In ISCAMI 2016 Book of Abstracts. 2016.

Základní 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

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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

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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.