A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images

The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as th...

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Published in:	Journal of digital imaging Vol. 36; no. 6; pp. 2480 - 2493
Main Authors:	Vinothini, R., Niranjana, G., Yakub, Fitri
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.12.2023 Springer Nature B.V
Subjects:	Algorithms Classification Computed tomography Coronaviruses COVID-19 COVID-19 - diagnostic imaging Deep learning Design standards Feature extraction Humans Image processing Image segmentation Imaging Long short-term memory Machine learning Medical imaging Medicine Medicine & Public Health Network design Neural networks Neural Networks, Computer Optimization Pandemics Parameters Radiology Respiratory system Segmentation Tomography, X-Ray Computed Viral diseases Computerized tomography scan images Pelican optimization algorithm Coronavirus No new U-Net High-resolution net Disease prediction
ISSN:	0897-1889, 1618-727X, 1618-727X
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Abstract	The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as the complexities involved in classifying infection regions. Currently, various deep learning (DL)-based methods are being introduced for the automatic detection of COVID-19 using computerized tomography (CT) scan images. In this paper, we propose the pelican optimization algorithm-based long short-term memory (POA-LSTM) method for classifying coronavirus using CT scan images. The data preprocessing technique is used to convert raw image data into a suitable format for subsequent steps. Here, we develop a general framework called no new U-Net (nnU-Net) for region of interest (ROI) segmentation in medical images. We apply a set of heuristic guidelines derived from the domain to systematically optimize the ROI segmentation task, which represents the dataset’s key properties. Furthermore, high-resolution net (HRNet) is a standard neural network design developed for feature extraction. HRNet chooses the top-down strategy over the bottom-up method after considering the two options. It first detects the subject, generates a bounding box around the object and then estimates the relevant feature. The POA is used to minimize the subjective influence of manually selected parameters and enhance the LSTM’s parameters. Thus, the POA-LSTM is used for the classification process, achieving higher performance for each performance metric such as accuracy, sensitivity, F1-score, precision, and specificity of 99%, 98.67%, 98.88%, 98.72%, and 98.43%, respectively.
AbstractList	The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as the complexities involved in classifying infection regions. Currently, various deep learning (DL)-based methods are being introduced for the automatic detection of COVID-19 using computerized tomography (CT) scan images. In this paper, we propose the pelican optimization algorithm-based long short-term memory (POA-LSTM) method for classifying coronavirus using CT scan images. The data preprocessing technique is used to convert raw image data into a suitable format for subsequent steps. Here, we develop a general framework called no new U-Net (nnU-Net) for region of interest (ROI) segmentation in medical images. We apply a set of heuristic guidelines derived from the domain to systematically optimize the ROI segmentation task, which represents the dataset's key properties. Furthermore, high-resolution net (HRNet) is a standard neural network design developed for feature extraction. HRNet chooses the top-down strategy over the bottom-up method after considering the two options. It first detects the subject, generates a bounding box around the object and then estimates the relevant feature. The POA is used to minimize the subjective influence of manually selected parameters and enhance the LSTM's parameters. Thus, the POA-LSTM is used for the classification process, achieving higher performance for each performance metric such as accuracy, sensitivity, F1-score, precision, and specificity of 99%, 98.67%, 98.88%, 98.72%, and 98.43%, respectively. The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as the complexities involved in classifying infection regions. Currently, various deep learning (DL)-based methods are being introduced for the automatic detection of COVID-19 using computerized tomography (CT) scan images. In this paper, we propose the pelican optimization algorithm-based long short-term memory (POA-LSTM) method for classifying coronavirus using CT scan images. The data preprocessing technique is used to convert raw image data into a suitable format for subsequent steps. Here, we develop a general framework called no new U-Net (nnU-Net) for region of interest (ROI) segmentation in medical images. We apply a set of heuristic guidelines derived from the domain to systematically optimize the ROI segmentation task, which represents the dataset's key properties. Furthermore, high-resolution net (HRNet) is a standard neural network design developed for feature extraction. HRNet chooses the top-down strategy over the bottom-up method after considering the two options. It first detects the subject, generates a bounding box around the object and then estimates the relevant feature. The POA is used to minimize the subjective influence of manually selected parameters and enhance the LSTM's parameters. Thus, the POA-LSTM is used for the classification process, achieving higher performance for each performance metric such as accuracy, sensitivity, F1-score, precision, and specificity of 99%, 98.67%, 98.88%, 98.72%, and 98.43%, respectively.The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as the complexities involved in classifying infection regions. Currently, various deep learning (DL)-based methods are being introduced for the automatic detection of COVID-19 using computerized tomography (CT) scan images. In this paper, we propose the pelican optimization algorithm-based long short-term memory (POA-LSTM) method for classifying coronavirus using CT scan images. The data preprocessing technique is used to convert raw image data into a suitable format for subsequent steps. Here, we develop a general framework called no new U-Net (nnU-Net) for region of interest (ROI) segmentation in medical images. We apply a set of heuristic guidelines derived from the domain to systematically optimize the ROI segmentation task, which represents the dataset's key properties. Furthermore, high-resolution net (HRNet) is a standard neural network design developed for feature extraction. HRNet chooses the top-down strategy over the bottom-up method after considering the two options. It first detects the subject, generates a bounding box around the object and then estimates the relevant feature. The POA is used to minimize the subjective influence of manually selected parameters and enhance the LSTM's parameters. Thus, the POA-LSTM is used for the classification process, achieving higher performance for each performance metric such as accuracy, sensitivity, F1-score, precision, and specificity of 99%, 98.67%, 98.88%, 98.72%, and 98.43%, respectively.
Author	Niranjana, G. Vinothini, R. Yakub, Fitri
Author_xml	– sequence: 1 givenname: R. surname: Vinothini fullname: Vinothini, R. email: vr8004@srmist.edu.in organization: Department of Computing Technologies, SRM Institute of Science and Technology – sequence: 2 givenname: G. surname: Niranjana fullname: Niranjana, G. organization: SRM Institute of Science and Technology – sequence: 3 givenname: Fitri surname: Yakub fullname: Yakub, Fitri organization: Electronic System Engineering Department, Malaysia-Japan International Institute of Technology
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Cites_doi	10.1016/j.asoc.2022.109683 10.1016/j.asoc.2020.106885 10.1002/ima.22627 10.1016/j.bspc.2021.103076 10.1016/j.clinimag.2021.02.003 10.1080/02286203.2021.1934797 10.1109/TCBB.2021.3065361 10.1016/j.compbiomed.2021.105127 10.1016/j.eswa.2022.116554 10.1016/j.bspc.2021.102920 10.1007/s42979-021-00690-w 10.1016/j.chaos.2020.110190 10.3390/s22030855 10.1016/j.imu.2020.100412 10.1016/j.patcog.2021.108499 10.1007/s00521-020-05410-8 10.34133/2021/8786793 10.1016/j.imu.2021.100540 10.1007/s12652-021-03464-7 10.1016/j.engappai.2023.105820 10.1007/s12559-020-09787-5 10.1007/s10489-020-01965-0
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Snippet	The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people...
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SubjectTerms	Algorithms Classification Computed tomography Coronaviruses COVID-19 COVID-19 - diagnostic imaging Deep learning Design standards Feature extraction Humans Image processing Image segmentation Imaging Long short-term memory Machine learning Medical imaging Medicine Medicine & Public Health Network design Neural networks Neural Networks, Computer Optimization Pandemics Parameters Radiology Respiratory system Segmentation Tomography, X-Ray Computed Viral diseases
Title	A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images
URI	https://link.springer.com/article/10.1007/s10278-023-00852-7 https://www.ncbi.nlm.nih.gov/pubmed/37491543 https://www.proquest.com/docview/2878556517 https://www.proquest.com/docview/2842451984 https://pubmed.ncbi.nlm.nih.gov/PMC10584759
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