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Viral pneumonia detection during the COVID-19 pandemic using deep learning and DCGAN-based data augmentation.
Betelhem Zewdu Wubineh1,2, Andrzej Rusiecki3, Krzysztof Halawa3
1Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wroclaw, Poland. betelhem.wubineh@pwr.edu.pl.
Scientific Reports
|May 19, 2026
Summary
This study enhances viral pneumonia detection in chest X-rays using deep learning and data augmentation. Combining Generative Adversarial Networks with ensemble methods significantly improves accuracy for automated diagnosis.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Automated chest X-ray analysis shows promise for respiratory disease diagnosis.
- Viral pneumonia detection is challenging due to radiographic overlap with COVID-19 and limited data.
Purpose of the Study:
- To detect viral pneumonia in chest X-ray images using Convolutional Neural Networks (CNNs), MobileNet, and ensemble learning.
- To address dataset limitations using Deep Convolutional Generative Adversarial Network (DCGAN)-based data augmentation.
Main Methods:
- Utilized 317 real chest X-ray images and generated 1,500 synthetic images via DCGAN.
- Conducted two experiments: (1) training/testing on original data, (2) training on combined real/synthetic data with evaluation on real images.
- Employed CNNs, MobileNet, and an ensemble approach for classification.
Main Results:
- The second experiment, combining real and synthetic data, yielded improved performance.
- An improved CNN achieved 92.4% accuracy, MobileNet reached 96.97%, and the ensemble method attained 96.97%.
- DCGAN-based augmentation combined with ensemble learning enhanced classification performance and robustness.
Conclusions:
- The proposed approach effectively supports automated viral pneumonia detection from chest X-ray images.
- DCGAN-based augmentation and ensemble learning are crucial for improving diagnostic accuracy and robustness.
- This method offers a valuable tool for supporting clinicians, especially during pandemics like COVID-19.
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