Classification of X-ray images into COVID-19, pneumonia, and TB using cGAN and fine-tuned deep transfer learning models

ABSTRACT:

Purpose

The rapid increase in the spread of the coronavirus disease of 2019 (COVID-19) has led to a need for reliable, effective, and readily available testing on a large scale. While diagnostic testing has been a support to public health, newer technology can be used to provide low-cost and convenient test options for patients. X-ray scanning can be performed to resolve this issue and produce quicker and more precise results. Currently, a radiologist is required to examine these X-ray images. However, deep convolutional neural networks can also be used to perform X-ray examinations and employed for the detection of COVID-19. We propose a Conditional Generative Adversarial Network (cGAN) with a fine-tuned deep transfer learning model to classify chest X-rays into six categories: C

SUBMITTER: Mehta T 

PROVIDER: S-EPMC8494631 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature