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Thorough Analysis of Deep Learning Methods for Diagnosis of COVID-19 CT Images
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Author(s): Gnaneswari Gnanaguru (CMR Institute of Technology, India), S.Silvia Priscila (Bharath Institute of Higher Education and Research, India), M. Sakthivanitha (Vels Institute of Science, Technology, and Advanced Studies, India), Sangeetha Radhakrishnan (Vels Institute of Science, Technology, and Advanced Studies, India), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)and Sonia Singh (Toss Global Management, UAE)
Copyright: 2024
Pages: 20
Source title:
Advancements in Clinical Medicine
Source Author(s)/Editor(s): P. Paramasivan (Dhaanish Ahmed College of Engineering, India), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India), Karthikeyan Chinnusamy (Veritas, USA), R. Regin (SRM Instıtute of Science and Technology, India)and Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand)
DOI: 10.4018/979-8-3693-5946-4.ch004
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Abstract
Since March 2020, WHO has classified COVID-19 a pandemic. This respiratory-system-focused viral infection causes atypical pneumonia. Experts stress the necessity of early COVID-19 detection. Isolating affected people is essential to stopping the virus. Early identification and efficient tracking are crucial for treatment and transmission reduction due to urgency. CT scans are fast and accurate COVID-19 screening tools. Using these scans to classify COVID-19 requires a radiologist, which can prolong the process. This chapter examines common deep learning (DL) techniques for COVID-19 detection. Their use in image processing is explored to improve diagnostics. Deep learning, a subset of machine learning (ML), can automate screening with medical practitioners to improve diagnostic accuracy and efficiency. The review discusses DL methods' pros and cons and their importance in radiologists' and doctors' collaboration.
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