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...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Science and Business Media Deutschland GmbH
2022
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| 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 |
| _version_ | 1796996097076363264 |
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| 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 |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T13:11:56Z |
| format | Conference or Workshop Item |
| id | UMPir39633 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T13:11:56Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | UMPir396332023-12-13T03:49:55Z 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 |
| 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 |
| title | 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_short | 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 |
| topic | Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| 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 |
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