Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees-Reference-Cited by-同舟云学术

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Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees

Published:2023-01 Issue: Volume:133 Page:109926
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Custode Leonardo Lucio,Mento Federico,Tursi Francesco,Smargiassi Andrea,Inchingolo Riccardo,Perrone Tiziano,Demi Libertario,Iacca Giovanni^ORCID

Publisher

Elsevier BV

Subject

Software

Reference26 articles.

1. Is there a role for lung ultrasound during the COVID-19 pandemic?;Soldati;J. Ultrasound Med.,2020

2. Proposal for international standardization of the use of lung ultrasound for patients with COVID-19;Soldati;J. Ultrasound Med.,2020

3. Can lung US help critical care clinicians in the early diagnosis of novel coronavirus (COVID-19) pneumonia?;Poggiali;Radiology,2020

4. COVID-19 pneumonia manifestations at the admission on chest ultrasound, radiographs, and CT: single-center study and comprehensive radiologic literature review;Lomoro;Eur. J. Radiol. Open,2020

5. Point-of-care chest ultrasonography as a diagnostic resource for COVID-19 outbreak in nursing homes;Nouvenne;J. Am. Med. Direct. Assoc.,2020

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Knowledge fused latent representation from lung ultrasound examination for COVID-19 pneumonia severity assessment;Ultrasonics;2024-09

2. Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare;Medicine Advances;2024-08-27

3. Low-complexity lung ultrasound video scoring by means of intensity projection-based video compression;Computers in Biology and Medicine;2024-02

4. A deep neural network with modified random forest incremental interpretation approach for diagnosing diabetes in smart healthcare;Applied Soft Computing;2024-02

5. Efficient evolution of decision trees via fully matrix-based fitness evaluation;Applied Soft Computing;2024-01

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