With the increasing demand for doctors in chest related diseases, there is a 15% performance gap every five years. If this gap is not filled with effective chest disease detection automation, the healthcare industry may face unfavorable consequences. There are only several studies that targeted X-ray images of cardiothoracic diseases. Most of the studies only targeted a single disease, which is inadequate. Although some related studies have provided an identification framework for all classes, the results are not encouraging due to a lack of data and imbalanced data issues. This research provides a significant contribution to Generative Adversarial Network (GAN) based synthetic data and four different types of deep learning-based models that provided comparable results. The models include a ResNet-152 model with image augmentation with an accuracy of 67%, a ResNet-152 model without image augmentation with an accuracy of 62%, transfer learning with Inception-V3 with an accuracy of 68%, and finally ResNet-152 model with image augmentation but targeted only six classes with an accuracy of 83%.

GAN; CNN; chest diseases; inception-V3; ResNet152