Thorough Analysis of Deep Learning Methods for Diagnosis of COVID-19 CT Images

Author:

Gnanaguru Gnaneswari1,Priscila S.Silvia2,Sakthivanitha M.3,Radhakrishnan Sangeetha3,Rajest S. Suman4ORCID,Singh Sonia5

Affiliation:

1. CMR Institute of Technology, India

2. Bharath Institute of Higher Education and Research, India

3. Vels Institute of Science, Technology, and Advanced Studies, India

4. Dhaanish Ahmed College of Engineering, India

5. Toss Global Management, UAE

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.

Publisher

IGI Global

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep Neural Network for Brain Tumour Segmentation Using Guaranteed Time Slots (GTS) Algorithm;Advances in Computer and Electrical Engineering;2024-08-30

2. Innovative Image Processing Methods for Colorectal Tumor Identification;Advances in Computer and Electrical Engineering;2024-08-30

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