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

Design of Hybrid Deep Learning Approach for Covid-19 Infected Lung Image Segmentation M. R-Prabha Ramkumar Prabhu SU. Suganthi S. Sridevi G.A. Senthil D. Vijendra Babu 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.
10 01 2021 012016 http://dx.doi.org/10.1088/crossmark-policy iopscience.iop.org Design of Hybrid Deep Learning Approach for Covid-19 Infected Lung Image Segmentation Journal of Physics: Conference Series paper Published under licence by IOP Publishing Ltd http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining 10.1088/1742-6596/2040/1/012016 https://iopscience.iop.org/article/10.1088/1742-6596/2040/1/012016 https://iopscience.iop.org/article/10.1088/1742-6596/2040/1/012016/pdf https://iopscience.iop.org/article/10.1088/1742-6596/2040/1/012016/pdf https://iopscience.iop.org/article/10.1088/1742-6596/2040/1/012016 https://iopscience.iop.org/article/10.1088/1742-6596/2040/1/012016/pdf Farid 2020 A Novel Approach of CT Images Feature Analysis and Prediction to Screen for CoronaVirus Disease (COVID-19) Gozes 2020 Rapid AI development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis Gozes 2020 Coronavirus Detection and Analysis on Chest CT with Deep Learning Herath 2020 Deep Learning Approach to Recognition of Novel COVID-19 Using CT Scans and Digital Image Processing King 2020 Purohit 10.1101/2020.07.15.205567 2020 IEEE Access Shaoping 8 2020 Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images Tello-Mijares 2021 10.1155/2021/8869372 Computed Tomography Image Processing Analysis in COVID-19 Patient Follow-Up Assessment Thangamani 2021 10.1016/j.micpro.2021.104043 IoT Defense Machine Learning: Emerging Solutions and Future Problems International Journal of Pure and Applied Mathematics Venkatachalam 119 2018 Effective Feature Set Selection And Centroid Classifier Algorithm For Web Services Discovery Deepak 2021 Prabha 2021 Karthikeyan 2021 Balajee 2021