Design of Hybrid Deep Learning Approach for Covid-19 Infected Lung Image Segmentation

R-Prabha, M. and Prabhu, Ramkumar and Suganthi, SU. and Sridevi, S. and Senthil, G.A. and Vijendra Babu, D. (2021) Design of Hybrid Deep Learning Approach for Covid-19 Infected Lung Image Segmentation. Journal of Physics: Conference Series, 2040 (1). 012016. ISSN 1742-6588

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Abstract

Lung infection or sickness is one of the most common acute ailments in humans. Pneumonia is one of the most common lung infections, and the annual global mortality rate from untreated pneumonia is increasing. Because of its rapid spread, pneumonia caused by the Coronavirus Disease (COVID-19) has emerged as a global danger as of December 2019. At the clinical level, the COVID-19 is frequently measured using a Computed Tomography Scan Slice (CTS) or a Chest X-ray. The goal of this study is to develop an image processing method for analyzing COVID-19 infection in CTS patients. The images in this study were preprocessed using the Hybrid Swarm Intelligence and Fuzzy DPSO algorithms. The findings suggest that the proposed method is more dependable, accurate, and simple than existing methods. © 2021 Elsevier B.V., All rights reserved.

Item Type: Article
Subjects: Computer Science > Artificial Intelligence
Divisions: Engineering and Technology > Aarupadai Veedu Institute of Technology, Chennai > Computer Science Engineering
Depositing User: Unnamed user with email techsupport@mosys.org
Last Modified: 03 Dec 2025 11:55
URI: https://vmuir.mosys.org/id/eprint/3095

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