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Critical Reviews™ in Biomedical Engineering

Published 6 issues per year

ISSN Print: 0278-940X

ISSN Online: 1943-619X

SJR: 0.262 SNIP: 0.372 CiteScore™:: 2.2 H-Index: 56

Indexed in

CNN Features and Optimized Generative Adversarial Network for COVID-19 Detection from Chest X-Ray Images

Volume 50, Issue 3, 2022, pp. 1-17
DOI: 10.1615/CritRevBiomedEng.2022042286
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ABSTRACT

Coronavirus is a RNA type virus, which makes various respiratory infections in both human as well as animals. In addition, it could cause pneumonia in humans. The Coronavirus affected patients has been increasing day to day, due to the wide spread of diseases. As the count of corona affected patients increases, most of the regions are facing the issue of test kit shortage. In order to resolve this issue, the deep learning approach provides a better solution for automatically detecting the COVID-19 disease. In this research, an optimized deep learning approach, named Henry gas water wave optimization-based deep generative adversarial network (HGWWO-Deep GAN) is developed. Here, the HGWWO algorithm is designed by the hybridization of Henry gas solubility optimization (HGSO) and water wave optimization (WWO) algorithm. The pre-processing method is carried out using region of interest (RoI) and median filtering in order to remove the noise from the images. Lung lobe segmentation is carried out using U-net architecture and lung region extraction is done using convolutional neural network (CNN) features. Moreover, the COVID-19 detection is done using Deep GAN trained by the HGWWO algorithm. The experimental result demonstrates that the developed model attained the optimal performance based on the testing accuracy of 0.9169, sensitivity of 0.9328, and specificity of 0.9032.

Figures

  • Block diagram of COVID-19 prediction based on proposed HGWWO-based Deep GAN approach (reprinted
from Minaee et al. with permission from Elsevier, copyright 2020)
  • U-net architecture (reprinted from Minaee et al. with permission from Elsevier, copyright 2020)
  • Extraction of CNN features (reprinted from Minaee et al. with permission from Elsevier, copyright 2020)
  • Structure of Deep GAN
  • Experimental outcomes of developed method: (a) Original image; (b) pre-processed median flter image; (c)
pre-processed ROI image; and (d) lobe segmented image (reprinted from Minaee et al. with permission from Elsevier,
copyright 2020)
  • Performance assessment of the developed technique using training data: (a) testing accuracy; (b) sensitivity;
and (c) specifcity
  • Performance assessment of the developed technique using k-fold value: (a) testing accuracy; (b) sensitivity;
and (c) specifcity
  • Analysis of the developed method considering training data: (a) testing accuracy; (b) sensitivity; and (c)
specifcity
  • Analysis of the developed method considering k-fold: (a) testing accuracy; (b) sensitivity; and (c) specifcity
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