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...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 6; pp. 2480 - 2494 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173050929&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173050929 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2023 vid: 36 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 173050929 173050929 173050929 10.1007/s10278-023-00852-7 173050929 ppf: 2480 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images. aug: au: Vinothini, R. Niranjana, G. Yakub, Fitri affil: https://ror.org/050113w36 Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, India sug: subj: Long Short-Term Memory COVID-19 Classification Image Interpretation, Computer Assisted Prediction Models Deep Learning Algorithms ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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