Advancing chest X-ray diagnostics: A novel CycleGAN-based preprocessing approach for enhanced lung disease classification in ChestX-Ray14

Abstract

Background and objective:

Chest radiography is a medical imaging technique widely used to diagnose thoracic diseases. However, X-ray images may contain artifacts such as irrelevant objects, medical devices, wires and electrodes that can introduce unnecessary noise, making difficult the distinction of relevant anatomical structures, and hindering accurate diagnoses. We aim in this study to address the issue of these artifacts in order to improve lung diseases classification results.

Methods:

In this paper we present a novel preprocessing approach which begins by detecting images that contain artifacts and then we reduce the artifacts’ noise effect by generating sharper images using a CycleGAN model. The DenseNet-121 model, used for the classification, incorporates channel and spatial attention mechanisms to specifically focus on relevant parts of the image. Additional information contained in the dataset, namely clinical characteristics, were also integrated into the model.

Results:

We evaluated the performance of the classification model before and after applying our proposed artifact preprocessing approach. These results clearly demonstrate that our preprocessing approach significantly improves the model’s AUC by 5.91% for pneumonia and 6.44% for consolidation classification, outperforming previous studies for the 14 diseases in the ChestX-Ray14 dataset.

Conclusion:

This research highlights the importance of considering the presence of artifacts when diagnosing lung diseases from radiographic images. By eliminating unwanted noise, our approach enables models to focus on relevant diagnostic features, thereby improving their performance. The results demonstrated that our approach is promising, highlighting its potential for broader applications in lung disease classification.