An Explainable Deep Learning-Based Method for Schizophrenia Diagnosis Using Generative Data-Augmentation

Abstract

Schizophrenia is an example of a rare mental disorder that is challenging to diagnose using conventional methods. Deep learning methods have been extensively employed to aid in the diagnosis of schizophrenia. However, their efficacy relies heavily on data quantity, and their black-box nature raises trust concerns, especially in medical diagnosis contexts. In this study, we leverage a deep learning-based method for the automatic diagnosis of schizophrenia using EEG brain recordings. This approach utilizes generative data augmentation, a powerful technique that enhances the accuracy of the diagnosis. Additionally, our study provides a framework to use when dealing with the challenge of limited training data for the diagnosis of other potential rare mental disorders. To enable the utilization of time-frequency features, spectrograms were extracted from the raw signals. After exploring several neural network architectural setups, a proper convolutional neural network (CNN) was used for the initial diagnosis. Subsequently, using Wasserstein GAN with Gradient Penalty (WGAN-GP) and Variational Autoencoder (VAE), two different synthetic datasets were generated in order to augment the initial dataset and address the over-fitting issue. The augmented dataset using VAE achieved a 3.0% improvement in accuracy, reaching 99.0%, and also demonstrated faster convergence. Finally, we addressed the lack of trust in black-box models using the Local Interpretable Model-agnostic Explanations (LIME) algorithm to determine the most important superpixels (frequencies) in the diagnosis process.

Keywords

• Medical diagnosis
• schizophrenia
• generative data augmentation
• variational autoencoder
• generative adversarial networks
• explainable artificial intelligence (XAI)