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Identification of Thoracic Diseases by Exploiting Deep Neural Networks

Saleh Albahli1, Hafiz Tayyab Rauf2,*, Muhammad Arif3, Md Tabrez Nafis4, Abdulelah Algosaibi5

1 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
2 Department of Computer Science, Univesity of Gurjat, 52250, Pakistan
3 School of Computer Science, Guanzghou University, Guangzhou, 510006, China
4 Department of Computer Science & Engineering, Jamia Hamdard, New Delhi, India
5 Department of Computer Science, CCSIT, King Faisal University, KSA

* Corresponding Author: Hafiz Tayyab Rauf. Email: email

(This article belongs to this Special Issue: AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems)

Computers, Materials & Continua 2021, 66(3), 3139-3149.


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%.


Cite This Article

S. Albahli, H. Tayyab Rauf, M. Arif, M. Tabrez Nafis and A. Algosaibi, "Identification of thoracic diseases by exploiting deep neural networks," Computers, Materials & Continua, vol. 66, no.3, pp. 3139–3149, 2021.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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