GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest

Abstract COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, m...

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Autores principales:	Pritam Saha, Debadyuti Mukherjee, Pawan Kumar Singh, Ali Ahmadian, Massimiliano Ferrara, Ram Sarkar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/10b00330893d4d0883189f2911eec646
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spelling	oai:doaj.org-article:10b00330893d4d0883189f2911eec6462021-12-02T14:26:15ZGraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest10.1038/s41598-021-87523-12045-2322https://doaj.org/article/10b00330893d4d0883189f2911eec6462021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87523-1https://doaj.org/toc/2045-2322Abstract COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, many research attempts have been made by the computer scientists to screen the COVID-19 from Chest X-Rays (CXRs) or Computed Tomography (CT) scans. To this end, we have proposed GraphCovidNet, a Graph Isomorphic Network (GIN) based model which is used to detect COVID-19 from CT-scans and CXRs of the affected patients. Our proposed model only accepts input data in the form of graph as we follow a GIN based architecture. Initially, pre-processing is performed to convert an image data into an undirected graph to consider only the edges instead of the whole image. Our proposed GraphCovidNet model is evaluated on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-Ray Images (Pneumonia) dataset and CMSC-678-ML-Project dataset. The model shows an impressive accuracy of 99% for all the datasets and its prediction capability becomes 100% accurate for the binary classification problem of detecting COVID-19 scans. Source code of this work can be found at GitHub-link .Pritam SahaDebadyuti MukherjeePawan Kumar SinghAli AhmadianMassimiliano FerraraRam SarkarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pritam Saha Debadyuti Mukherjee Pawan Kumar Singh Ali Ahmadian Massimiliano Ferrara Ram Sarkar GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest
description	Abstract COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, many research attempts have been made by the computer scientists to screen the COVID-19 from Chest X-Rays (CXRs) or Computed Tomography (CT) scans. To this end, we have proposed GraphCovidNet, a Graph Isomorphic Network (GIN) based model which is used to detect COVID-19 from CT-scans and CXRs of the affected patients. Our proposed model only accepts input data in the form of graph as we follow a GIN based architecture. Initially, pre-processing is performed to convert an image data into an undirected graph to consider only the edges instead of the whole image. Our proposed GraphCovidNet model is evaluated on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-Ray Images (Pneumonia) dataset and CMSC-678-ML-Project dataset. The model shows an impressive accuracy of 99% for all the datasets and its prediction capability becomes 100% accurate for the binary classification problem of detecting COVID-19 scans. Source code of this work can be found at GitHub-link .
format	article
author	Pritam Saha Debadyuti Mukherjee Pawan Kumar Singh Ali Ahmadian Massimiliano Ferrara Ram Sarkar
author_facet	Pritam Saha Debadyuti Mukherjee Pawan Kumar Singh Ali Ahmadian Massimiliano Ferrara Ram Sarkar
author_sort	Pritam Saha
title	GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest
title_short	GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest
title_full	GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest
title_fullStr	GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest
title_full_unstemmed	GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest
title_sort	graphcovidnet: a graph neural network based model for detecting covid-19 from ct scans and x-rays of chest
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/10b00330893d4d0883189f2911eec646
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GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest

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