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

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

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Abstract

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.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Chest X-ray; Convolutional neural network; COVID-19; DenseNet
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division:	Institute of Postgraduate Studies College of Engineering Faculty of Computing
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	13 Dec 2023 03:49
Last Modified:	13 Dec 2023 03:49
URI:	http://umpir.ump.edu.my/id/eprint/39633
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