Saleh Albahli1, Hafiz Tayyab Rauf2,*, Muhammad Arif3, Md Tabrez Nafis4, Abdulelah Algosaibi5
CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3139-3149, 2021, DOI:10.32604/cmc.2021.014134
Abstract 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)… More >
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