NDLI: A Critical Review of Machine Learning Techniques for Diagnosing the Corona Virus Disease (COVID-19)

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A Critical Review of Machine Learning Techniques for Diagnosing the Corona Virus Disease (COVID-19)

Content Provider	Scilit
Author	Kumari, Khushbu Das, Rik Manjhi, Pankaj Kumar Singh, Satya Narayan
Copyright Year	2021
Description	Handling and combatting the alarming spread of Corona Virus Disease (COVID-19) has been the prime concern of global authorities in recent times. Detection and diagnosis of this disease are major challenges for medical and health personnel due to lack of previous knowledge about dealing this “novel” outbreak. Researchers are attempting to deploy highly efficient machine learning and deep learning techniques for effective identification of COVID-19. The traditional radiological X-ray, CT scanning and magnetic resonance imaging (MRI) techniques are coupled with intelligence-based learning systems to leverage efficient dealing with the detection and diagnosis of the COVID cases with better monitoring approaches. This chapter has reviewed recent machine learning and deep learning-based approaches for detection of COVID. The authors have endeavored to bring assorted propositions under one roof to assist researchers in comparing efficiencies of diverse ML techniques for COVID-19 detection. This will create more opportunities for better analyzing and understanding the machine learning and deep based approaches for dealing with COVID-19 and similar unknown threats in times to come. Book Name: Disruptive Trends in Computer Aided Diagnosis
Related Links	https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003045816-6&type=chapterpdf
Ending Page	92
Page Count	10
Starting Page	83
DOI	10.1201/9781003045816-6
Language	English
Publisher	Informa UK Limited
Publisher Date	2021-08-11
Access Restriction	Open
Subject Keyword	Book Name: Disruptive Trends in Computer Aided Diagnosis Machine Learning Corona Efficient Diverse Learning and Deep Detection
Content Type	Text
Resource Type	Chapter

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