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 f...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%.展开更多
文摘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%.
