On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions

Abstract

Schizophrenia is a serious mental disorder with a complex neurobiological background and a well-defined psychopathological picture. Despite many efforts, a definitive disease biomarker has still not been identified. One of the promising candidates for a disease-related biomarker could involve retinal morphology , given that the retina is a part of the central nervous system that is known to be affected in schizophrenia and related to multiple illness features. In this study Optical Coherence Tomography (OCT) data is applied to assess the different layers of the retina. OCT data were applied in the process of automatic differentiation of schizophrenic patients from healthy controls. Numerical experiments involved applying several individual 1D Convolutional Neural Network-based models as well as further using the aggregation of classification results to improve the initial classification results. The main goal of the study was to check how methods based on the aggregation of classification results work in classifying neuroanatomical features of schizophrenia. Among over 300, 000 different variants of tested aggregation operators, a few versions provided satisfactory results.

Figure 1

General overall scheme of the aggregation procedure.

Figure 2

Accuracy of the Quadrature-Inspired Smooth Generalized Choquet Integral in dependence on the parameter value.

Figure 3

Accuracy of the classical pre-aggregation function in dependence on the parameter value.

Figure 4

Standard deviations in dependence on the parameter values.

Figure 5

ROC curves with the AUC values for the aggregation operators denoted by the formulae numbers.