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

arXiv: https://arxiv.org/abs/2310.16867 · HTML: https://mlsanity.com/paper/2310.16867/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 12 of 12)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [FMRI data augmentation via synthesis](https://mlsanity.com/paper/1907.06134/) (2019)
- [Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models](https://mlsanity.com/paper/2109.01120/) (2021)
- [Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network](https://mlsanity.com/paper/1903.08858/) (2019)
- [Individual Recognition in Schizophrenia using Deep Learning Methods with Random Forest and Voting Classifiers: Insights from Resting State EEG Streams](https://mlsanity.com/paper/1707.03467/) (2017)
- [Parkinson's Disease Diagnosis based on Gait Cycle Analysis Through an Interpretable Interval Type-2 Neuro-Fuzzy System](https://mlsanity.com/paper/2109.02442/) (2021)
- [Medical Imaging and Computational Image Analysis in COVID-19 Diagnosis: A Review](https://mlsanity.com/paper/2010.02154/) (2020)
- [Machine learning techniques for the Schizophrenia diagnosis: A comprehensive review and future research directions](https://mlsanity.com/paper/2301.07496/) (2023)

## Built on by (most cited first, 4 of 4)

- [A Novel Multi-Task Teacher-Student Architecture with Self-Supervised Pretraining for 48-Hour Vasoactive-Inotropic Trend Analysis in Sepsis Mortality Prediction](https://mlsanity.com/paper/2502.16834/) (2025)
- [What Do EEG Foundation Models Capture from Human Brain Signals?](https://mlsanity.com/paper/2605.11410/) (2026)
- [SincPD: An Explainable Method based on Sinc Filters to Diagnose Parkinson's Disease Severity by Gait Cycle Analysis](https://mlsanity.com/paper/2502.17463/) (2025)
- [A Context-Aware Temporal Modeling through Unified Multi-Scale Temporal Encoding and Hierarchical Sequence Learning for Single-Channel EEG Sleep Staging](https://mlsanity.com/paper/2512.22976/) (2025)

## Abstract (arXiv)

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.