Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph-Reference-Cited by-同舟云学术

Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph

Published:2021-04 Issue: Volume:131 Page:104252
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Jin Weiqiu,Dong Shuqin,Dong Changzi^ORCID,Ye Xiaodan

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference26 articles.

1. Chest X-ray in new Coronavirus Disease 2019 (COVID-19) infection: findings and correlation with clinical outcome;Cozzi;Radiol. Med.,2020

2. Radiographic findings in 240 patients with COVID-19 pneumonia: time-dependence after the onset of symptoms;Vancheri;Eur. Radiol.,2020

3. A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks;Varela-Santos;Inf. Sci.,2021

4. Automated detection of COVID-19 cases using deep neural networks with X-ray images;Ozturk;Comput. Biol. Med.,2020

Cited by 53 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. A review of medical image-based diagnosis of COVID-19;Progress in Medical Devices;2023-12-31

2. A rapid literature review on ensemble algorithms for COVID-19 classification using image-based exams;International Journal of Hybrid Intelligent Systems;2023-11-03

3. The effectiveness of deep learning vs. traditional methods for lung disease diagnosis using chest X-ray images: A systematic review;Applied Soft Computing;2023-11

4. Pneumonia Detection Using Deep Learning Techniques;2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE);2023-11-01

5. Neural Networks for the Diagnosis of Covid-19 in Chest X-ray Images: A Systematic Review and Meta-Analysis;EAI Endorsed Transactions on Pervasive Health and Technology;2023-10-23