Unsupervised contrastive unpaired image generation approach for improving tuberculosis screening using chest X-ray images

Tuberculosis is an infectious disease that mainly affects the lung tissues. Therefore, chest X-ray imaging can be very useful to diagnose and understand evolution of pathology. This image modality has a poorer quality in contrast with other techniques as magnetic resonance or computerized tomography, but easier cheaper perform. Furthermore, data scarcity challenging domain biomedical imaging. In order mitigate this problem, use Generative Adversarial Network models for generation proved powerful approach train deep learning small datasets, representing alternative classic augmentation strategies. work, we propose fully automatic novel synthetic images effect improve tuberculosis screening performance using 3 different publicly available representative datasets: Montgomery County, Shenzhen TBX11K. Firstly, trains translation large-sized dataset (TBX11K). Then, these are used generate set small-sized medium-sized datasets (Montgomery County Shenzhen, respectively). Finally, generated added training screening. As result, obtained 88.41% ± 5.27% accuracy 90.33% 1.41% dataset. These results demonstrate proposed method outperforms previous state-of-the-art approaches.