DeepCSFusion: Deep Compressive Sensing Fusion for Efficient COVID-19 Classification.
Worldwide, the COVID-19 epidemic, which started in 2019, has resulted in millions of deaths. The medical research community has widely used computer analysis of medical data during the pandemic, specifically deep learning models. Deploying models on devices with constrained resources is a significan...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 4; pp. 1346 - 1359 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2024
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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=179554117&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179554117 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2024 vid: 37 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179554117 179554117 179554117 10.1007/s10278-024-01011-2 179554117 ppf: 1346 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: DeepCSFusion: Deep Compressive Sensing Fusion for Efficient COVID-19 Classification. aug: au: Ragab, Dina A. Fayed, Salema Ghatwary, Noha affil: Electronics & Communications Engineering Department, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Smart Village Campus, Giza, Egypt sug: subj: COVID-19 Diagnosis COVID-19 Classification COVID-19 Radiography Tomography, X-Ray Computed Deep Learning Diagnosis, Computer Assisted Image Processing, Computer Assisted Radiography, Thoracic Neural Networks (Computer) Decision Making, Computer Assisted Contrast Media Image Enhancement Early Diagnosis ab: Worldwide, the COVID-19 epidemic, which started in 2019, has resulted in millions of deaths. The medical research community has widely used computer analysis of medical data during the pandemic, specifically deep learning models. Deploying models on devices with constrained resources is a significant challenge due to the increased storage demands associated with larger deep learning models. Accordingly, in this paper, we propose a novel compression strategy that compresses deep features with a compression ratio of 10 to 90% to accurately classify the COVID-19 and non-COVID-19 computed tomography scans. Additionally, we extensively validated the compression using various available deep learning methods to extract the most suitable features from different models. Finally, the suggested DeepCSFusion model compresses the extracted features and applies fusion to achieve the highest classification accuracy with fewer features. The proposed DeepCSFusion model was validated on the publicly available dataset "SARS-CoV-2 CT" scans composed of 1252 CT. This study demonstrates that the proposed DeepCSFusion reduced the computational time with an overall accuracy of 99.3%. Also, it outperforms state-of-the-art pipelines in terms of various classification measures. pubtype: Academic Journal doctype: diagnostic images equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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