A Review of CNN on Medical Imaging to Diagnose COVID-19 Infections

8 pages•Published: November 2, 2021

Samuel Clark, Ehsan Kamalinejad, Christian Magpantay, Suritaneil Sahota, Jiaofei Zhong and Yanke Hu

Abstract

In this paper, we study the Convolutional Neural Network (CNN) applications in medical image processing during the battle against Coronavirus Disease 2019 (COVID- 19). Specifically, three CNN implementations are examined: CNN-LSTM, COVID-Net, and DeTraC. These three methods have been shown to offer promising implications for the future of CNN technology in the medical field. This survey explores how these technologies have improved upon their predecessors. Qualitative and quantitative analyses have strongly suggested that these methods perform significantly better than the commensurate technologies. After analyzing these CNN implementations, it is reasonable to conclude that this technology has a place in the future of the medical field, which can be used by professionals to gain insight into new diseases and to help in diagnosing infections using medical imaging.

Keyphrases: cnn, covid 19, image processing, x ray

In: Yan Shi, Gongzhu Hu, Quan Yuan and Takaaki Goto (editors). Proceedings of ISCA 34th International Conference on Computer Applications in Industry and Engineering, vol 79, pages 91-98.

Links:	https://easychair.org/publications/paper/pWNx
	https://doi.org/10.29007/6xsd

BibTeX entry

@inproceedings{CAINE2021:Review_CNN_Medical_Imaging,
  author    = {Samuel Clark and Ehsan Kamalinejad and Christian Magpantay and Suritaneil Sahota and Jiaofei Zhong and Yanke Hu},
  title     = {A Review of CNN on Medical Imaging to Diagnose COVID-19 Infections},
  booktitle = {Proceedings of ISCA 34th International Conference on Computer Applications in Industry and Engineering},
  editor    = {Yan Shi and Gongzhu Hu and Quan Yuan and Takaaki Goto},
  series    = {EPiC Series in Computing},
  volume    = {79},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/pWNx},
  doi       = {10.29007/6xsd},
  pages     = {91-98},
  year      = {2021}}

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