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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Veröffentlicht in:Journal of digital imaging Jg. 36; H. 6; S. 2480 - 2493
Hauptverfasser: Vinothini, R, Niranjana, G, Yakub, Fitri
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Springer Nature B.V 01.12.2023
Schlagworte:
Algorithms
Classification
Computed tomography
Coronaviruses
COVID-19
COVID-19 - diagnostic imaging
Deep learning
Design standards
Feature extraction
Humans
Image processing
Image segmentation
Long short-term memory
Machine learning
Medical imaging
Network design
Neural networks
Neural Networks, Computer
Optimization
Pandemics
Parameters
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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Zusammenfassung: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.
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ISSN:0897-1889
1618-727X
1618-727X
DOI:10.1007/s10278-023-00852-7