A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron-Reference-Cited by-同舟云学术

A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron

Published:2023-01-12 Issue: Volume: Page:1-43
ISSN:0952-813X
Container-title:Journal of Experimental & Theoretical Artificial Intelligence
language:en
Short-container-title:Journal of Experimental & Theoretical Artificial Intelligence

Author:

Khan Asifullah¹²³,Khan Saddam Hussain¹⁴,Saif Mahrukh¹,Batool Asiya¹,Sohail Anabia¹⁵,Waleed Khan Muhammad¹⁶

Affiliation:

1. Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, Pakistan

2. PIEAS Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan

3. Center for Mathematical Sciences, Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan

4. Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat, Pakistan

5. Department of Computer Science, Faculty of Computing & Artificial Intelligence, Air University, Islamabad, Pakistan

6. Department of Mechanical and Aerospace Engineering, Columbus, OH, USA

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Theoretical Computer Science,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/0952813X.2023.2165724

Reference179 articles.

1. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

2. Detection of the Omicron (B.1.1.529) variant of SARS-CoV-2 in aircraft wastewater

3. Ahmed, U., Khan, A., Khan, S. H., Basit, A., Haq, I. U. & Lee, Y. S. (2019). Transfer Learning and Meta Classification Based Deep Churn Prediction System for Telecom Industry. http://arxiv.org/abs/1901.06091

4. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases

5. COVID-19 Lesion Segmentation Using Lung CT Scan Images: Comparative Study Based on Active Contour Models

Cited by 32 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. Application of Artificial Intelligence in Cone-Beam Computed Tomography for Airway Analysis: A Narrative Review;Diagnostics;2024-08-30

2. Improving COVID‐19 Detection Through Cooperative Deep‐Learning Pipeline for Lung Semantic Segmentation in Medical Imaging;International Journal of Imaging Systems and Technology;2024-08-14

3. Novel Deep CNNs Explore Regions, Boundaries, and Residual Learning for COVID-19 Infection Analysis in Lung CT;Tomography;2024-08-03

4. Towards COVID-19 detection and classification using optimal efficient Densenet model on chest X-ray images;Alexandria Engineering Journal;2024-08

5. Comparative Analysis of Deep Convolution Neural Networks on Medical Image-Based COVID-19 Detection;2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN);2024-07-18