Open Access

ARTICLE

Identification of Thoracic Diseases by Exploiting Deep Neural Networks

by Saleh Albahli¹, Hafiz Tayyab Rauf^2,*, 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:

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

Computers, Materials & Continua 2021, 66(3), 3139-3149. https://doi.org/10.32604/cmc.2021.014134

Received 01 September 2020; Accepted 17 October 2020; Issue published 28 December 2020

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) 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%.

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