tetano
Editor, Senior Moderator
Healthcare (Basel)
. 2022 Feb 21;10(2):403.
doi: 10.3390/healthcare10020403.
COVID-19 Identification from Low-Quality Computed Tomography Using a Modified Enhanced Super-Resolution Generative Adversarial Network Plus and Siamese Capsule Network
Grace Ugochi Nneji[SUP] 1 [/SUP], Jianhua Deng[SUP] 1 [/SUP], Happy Nkanta Monday[SUP] 2 [/SUP], Md Altab Hossin[SUP] 3 [/SUP], Sandra Obiora[SUP] 3 [/SUP], Saifun Nahar[SUP] 4 [/SUP], Jingye Cai[SUP] 1 [/SUP]
Affiliations
Abstract
Computed Tomography has become a vital screening method for the detection of coronavirus 2019 (COVID-19). With the high mortality rate and overload for domain experts, radiologists, and clinicians, there is a need for the application of a computerized diagnostic technique. To this effect, we have taken into consideration improving the performance of COVID-19 identification by tackling the issue of low quality and resolution of computed tomography images by introducing our method. We have reported about a technique named the modified enhanced super resolution generative adversarial network for a better high resolution of computed tomography images. Furthermore, in contrast to the fashion of increasing network depth and complexity to beef up imaging performance, we incorporated a Siamese capsule network that extracts distinct features for COVID-19 identification.The qualitative and quantitative results establish that the proposed model is effective, accurate, and robust for COVID-19 screening. We demonstrate the proposed model for COVID-19 identification on a publicly available dataset COVID-CT, which contains 349 COVID-19 and 463 non-COVID-19 computed tomography images. The proposed method achieves an accuracy of 97.92%, sensitivity of 98.85%, specificity of 97.21%, AUC of 98.03%, precision of 98.44%, and F1 score of 97.52%. Our approach obtained state-of-the-art performance, according to experimental results, which is helpful for COVID-19 screening. This new conceptual framework is proposed to play an influential task in the issue facing COVID-19 and related ailments, with the availability of few datasets.
Keywords: Siamese network; adversarial learning; computed tomography; convolutional neural network; deep learning; super-resolution.
. 2022 Feb 21;10(2):403.
doi: 10.3390/healthcare10020403.
COVID-19 Identification from Low-Quality Computed Tomography Using a Modified Enhanced Super-Resolution Generative Adversarial Network Plus and Siamese Capsule Network
Grace Ugochi Nneji[SUP] 1 [/SUP], Jianhua Deng[SUP] 1 [/SUP], Happy Nkanta Monday[SUP] 2 [/SUP], Md Altab Hossin[SUP] 3 [/SUP], Sandra Obiora[SUP] 3 [/SUP], Saifun Nahar[SUP] 4 [/SUP], Jingye Cai[SUP] 1 [/SUP]
Affiliations
- PMID: 35207017
- DOI: 10.3390/healthcare10020403
Abstract
Computed Tomography has become a vital screening method for the detection of coronavirus 2019 (COVID-19). With the high mortality rate and overload for domain experts, radiologists, and clinicians, there is a need for the application of a computerized diagnostic technique. To this effect, we have taken into consideration improving the performance of COVID-19 identification by tackling the issue of low quality and resolution of computed tomography images by introducing our method. We have reported about a technique named the modified enhanced super resolution generative adversarial network for a better high resolution of computed tomography images. Furthermore, in contrast to the fashion of increasing network depth and complexity to beef up imaging performance, we incorporated a Siamese capsule network that extracts distinct features for COVID-19 identification.The qualitative and quantitative results establish that the proposed model is effective, accurate, and robust for COVID-19 screening. We demonstrate the proposed model for COVID-19 identification on a publicly available dataset COVID-CT, which contains 349 COVID-19 and 463 non-COVID-19 computed tomography images. The proposed method achieves an accuracy of 97.92%, sensitivity of 98.85%, specificity of 97.21%, AUC of 98.03%, precision of 98.44%, and F1 score of 97.52%. Our approach obtained state-of-the-art performance, according to experimental results, which is helpful for COVID-19 screening. This new conceptual framework is proposed to play an influential task in the issue facing COVID-19 and related ailments, with the availability of few datasets.
Keywords: Siamese network; adversarial learning; computed tomography; convolutional neural network; deep learning; super-resolution.