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

• URL: http://arxiv.org/abs/2310.16867v2
• Date: Tue, 16 Jul 2024 19:51:24 GMT
• Title: An Explainable Deep Learning-Based Method For Schizophrenia Diagnosis Using Generative Data-Augmentation
• Authors: Mehrshad Saadatinia, Armin Salimi-Badr,
• Abstract summary: 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.
• Score: 0.3222802562733786
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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. 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 up to 99.0\% and yielded a lower loss value as well as a 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.

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