Download PDFOpen PDF in browser

A Review on Automated Covid-19 Detection Using Deep Learning Architectures

EasyChair Preprint no. 9152

9 pagesDate: October 26, 2022


- COVID-19 is an acronym for coronavirus disease 2019. On 13th January 2020, the World Health Organization (WHO) declared it a pandemic. With increasingly COVID-19 instances, the to-be-had clinical infrastructure is vital to discover the suspected instances. Medical imaging strategies which include Computed Tomography (CT), and chest radiography can play a vital position withinside the early screening and detection of COVID-19 instances. It is vital to discover and separate the instances to prevent them, in addition to, the unfolding of the virus. Artificial Intelligence can play a vital position in COVID-19 detection and reduces the workload on collapsing clinical infrastructure. According to the look of this paper, inflamed sufferers have assorted radiographic visible traits in addition to dry cough, breathlessness, fever, and different symptoms. Therefore, computed tomography and X-ray photographs had been extensively used to be extra powerful in the analysis of this ailment. With this motivation, the evaluation of various authors is made to hurry up the detection and class of COVID-19 sufferers from different pneumonia groups. In those papers, distinct troubles like pneumonia or regular contamination aren't always immediately identified. In this paper, a deep convolutional neural network-primarily based totally structure is proposed for the COVID-19 detection of the usage of chest radiographs. In destiny, AI is applied to the usage of the MATLAB platform and outcomes might be as compared with current parameters.

Keyphrases: COVID-19, direct relapse, multi-aspect preceptor.

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ubair Ali and Reecha Sharma and Mandeep Kaur},
  title = {A Review on Automated Covid-19 Detection Using Deep Learning Architectures},
  howpublished = {EasyChair Preprint no. 9152},

  year = {EasyChair, 2022}}
Download PDFOpen PDF in browser