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A densely interconnected convolutional neural network-based approach to identify COVID-19 from Chest X-ray Images

The novel Corona Virus (COVID-19) has spread so rapidly that cause a devastating effect on public well-being and create an emergency around the world. Hence, the rapid identification of COVID-19 has become a challenging work within a short period. Clinical trials of patients with COVID-19 have shown...

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Main Authors: Alfaz, Nazia, Sarwar, Talha, Das, Argho, Noorhuzaimi, Mohd Noor
Format: Conference or Workshop Item
Language:English
English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
Q Science (General)
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/39633/1/A%20Densely%20Interconnected%20Convolutional%20Neural%20Network-Based%20Approach.pdf
http://umpir.ump.edu.my/id/eprint/39633/2/A%20densely%20interconnected%20convolutional%20neural%20network-based%20approach%20to%20identify%20COVID-19%20from%20Chest%20X-ray%20Images_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39633/
https://doi.org/10.1007/978-981-16-8129-5_65
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spelling my.ump.umpir.396332023-12-13T03:49:55Z http://umpir.ump.edu.my/id/eprint/39633/ A densely interconnected convolutional neural network-based approach to identify COVID-19 from Chest X-ray Images Alfaz, Nazia Sarwar, Talha Das, Argho Noorhuzaimi, Mohd Noor Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) The novel Corona Virus (COVID-19) has spread so rapidly that cause a devastating effect on public well-being and create an emergency around the world. Hence, the rapid identification of COVID-19 has become a challenging work within a short period. Clinical trials of patients with COVID-19 have shown that most of the patients affected by COVID-19 experience lung infection that can cause inflammation in the lung after virus-contiguity. It can damage the cells and tissue that is inside the lung. However, pneumonia is also a lung infection that can cause inflammation in the air sacs inside the lung. Chest X-rays and CT scans perform an essential role in the detection of lung-related illnesses. Therefore, concerning the diagnosis of COVID-19, radiography and chest CT are considered as fundamental imaging approaches. This study presents a densely interconnected convolutional neural network-based approach to identify COVID-19, Pneumonia and Normal patients from chest X-ray images. To experiment with the proposed methodology, a new dataset is generated by combining two different datasets from Kaggle named COVID-19 Radiography Database and Chest X-ray (COVID-19 & Pneumonia). The dataset comprises of 500 X-ray images of COVID-19 affected people, 2600 X-ray images of Normal people, and 3418 X-ray images of pneumonia affected people. The proposed densely interconnected convolutional neural network model produces 99% testing accuracy for COVID-19, 98% testing accuracy for Pneumonia and 98% testing accuracy for Normal people without the application of any augmentation techniques. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39633/1/A%20Densely%20Interconnected%20Convolutional%20Neural%20Network-Based%20Approach.pdf pdf en http://umpir.ump.edu.my/id/eprint/39633/2/A%20densely%20interconnected%20convolutional%20neural%20network-based%20approach%20to%20identify%20COVID-19%20from%20Chest%20X-ray%20Images_ABS.pdf Alfaz, Nazia and Sarwar, Talha and Das, Argho and Noorhuzaimi, Mohd Noor (2022) A densely interconnected convolutional neural network-based approach to identify COVID-19 from Chest X-ray Images. In: Lecture Notes in Electrical Engineering; 11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021 , 5-6 April 2021 , Virtual, Online. pp. 419-425., 829 LNEE (272139). ISSN 1876-1100 ISBN 978-981168128-8 https://doi.org/10.1007/978-981-16-8129-5_65
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic Q Science (General)
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle Q Science (General)
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Alfaz, Nazia
Sarwar, Talha
Das, Argho
Noorhuzaimi, Mohd Noor
A densely interconnected convolutional neural network-based approach to identify COVID-19 from Chest X-ray Images
description The novel Corona Virus (COVID-19) has spread so rapidly that cause a devastating effect on public well-being and create an emergency around the world. Hence, the rapid identification of COVID-19 has become a challenging work within a short period. Clinical trials of patients with COVID-19 have shown that most of the patients affected by COVID-19 experience lung infection that can cause inflammation in the lung after virus-contiguity. It can damage the cells and tissue that is inside the lung. However, pneumonia is also a lung infection that can cause inflammation in the air sacs inside the lung. Chest X-rays and CT scans perform an essential role in the detection of lung-related illnesses. Therefore, concerning the diagnosis of COVID-19, radiography and chest CT are considered as fundamental imaging approaches. This study presents a densely interconnected convolutional neural network-based approach to identify COVID-19, Pneumonia and Normal patients from chest X-ray images. To experiment with the proposed methodology, a new dataset is generated by combining two different datasets from Kaggle named COVID-19 Radiography Database and Chest X-ray (COVID-19 & Pneumonia). The dataset comprises of 500 X-ray images of COVID-19 affected people, 2600 X-ray images of Normal people, and 3418 X-ray images of pneumonia affected people. The proposed densely interconnected convolutional neural network model produces 99% testing accuracy for COVID-19, 98% testing accuracy for Pneumonia and 98% testing accuracy for Normal people without the application of any augmentation techniques.
format Conference or Workshop Item
author Alfaz, Nazia
Sarwar, Talha
Das, Argho
Noorhuzaimi, Mohd Noor
author_facet Alfaz, Nazia
Sarwar, Talha
Das, Argho
Noorhuzaimi, Mohd Noor
author_sort Alfaz, Nazia
title A densely interconnected convolutional neural network-based approach to identify COVID-19 from Chest X-ray Images
title_short A densely interconnected convolutional neural network-based approach to identify COVID-19 from Chest X-ray Images
title_full A densely interconnected convolutional neural network-based approach to identify COVID-19 from Chest X-ray Images
title_fullStr A densely interconnected convolutional neural network-based approach to identify COVID-19 from Chest X-ray Images
title_full_unstemmed A densely interconnected convolutional neural network-based approach to identify COVID-19 from Chest X-ray Images
title_sort densely interconnected convolutional neural network-based approach to identify covid-19 from chest x-ray images
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/39633/1/A%20Densely%20Interconnected%20Convolutional%20Neural%20Network-Based%20Approach.pdf
http://umpir.ump.edu.my/id/eprint/39633/2/A%20densely%20interconnected%20convolutional%20neural%20network-based%20approach%20to%20identify%20COVID-19%20from%20Chest%20X-ray%20Images_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39633/
https://doi.org/10.1007/978-981-16-8129-5_65
_version_ 1822923983530491904
score 13.235362

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