An intelligence design for detection and classification of COVID19 using fusion of classical and convolutional neural network and improved microscopic features selection approach-Reference-Cited by-同舟云学术

An intelligence design for detection and classification of COVID19 using fusion of classical and convolutional neural network and improved microscopic features selection approach

Published:2021-05-08 Issue:10 Volume:84 Page:2254-2267
ISSN:1059-910X
Container-title:Microscopy Research and Technique
language:en
Short-container-title:Microsc Res Tech

Author:

Amin Javaria¹,Anjum Muhammad Almas²,Sharif Muhammad³,Saba Tanzila⁴^ORCID,Tariq Usman⁵

Affiliation:

1. Department of Computer Science University of Wah Wah Pakistan

2. National University of Technology (NUTECH) IJP Road Islamabad Pakistan

3. Department of Computer Science COMSATS University Islamabad ‐ Wah Campus Wah Cantt Pakistan 4740 Pakistan

4. Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh Saudi Arabia

5. College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

Publisher

Wiley

Subject

Medical Laboratory Technology,Instrumentation,Histology,Anatomy

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jemt.23779

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3. Albahri O. Zaidan A. Albahri A. Zaidan B. Abdulkareem K. H. Al‐Qaysi Z. …Health P.(2020b).Systematic review of artificial intelligence techniques in the detection and classification of COVID‐19 medical images in terms of evaluation and benchmarking: Taxonomy analysis challenges future solutions and methodological aspects 13(10) 1381–1396.

4. Speeded-Up Robust Features (SURF)

5. Bagging predictors

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