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 candidate for such biomarker might be the retinal morphology. 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 and healthy controls. Numerical experiments covered 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.