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COVID-19 Prognosis from Chest X-ray Images by using Deep Learning Approaches: A Next Generation Diagnostic Tool

Medicine and Health

COVID-19 Prognosis from Chest X-ray Images by using Deep Learning Approaches: A Next Generation Diagnostic Tool

M. Pal, S. Parij, et al.

This exciting research conducted by Madhumita Pal, Smit Parij, Ganapati Pan, Snehasish Mishra, Ranjan K Mohapatra, and Kuldeep Dhama explores the powerful application of deep learning models, VGG-16 and LSTM, for accurate COVID-19 diagnosis from chest X-ray images. The findings illustrate impressive classification accuracy, making it a promising tool for swift COVID-19 screening in challenging environments.

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~3 min • Beginner • English
Abstract
Global public health is overwhelmed due to the ongoing COVID-19 pandemic. As of October 2022, SARS-CoV-2 and its variants have caused over 600 million confirmed cases and nearly 6.5 million deaths globally. This study aims to better understand COVID-19 infection from chest X-ray (CXR) images by classifying images of normal, pneumonia, and COVID-19 cases using deep learning models (VGG16 and LSTM) through feature extraction. During pandemic peaks, shortages of beds and specialist physicians impeded care; computer-aided analysis can aid early screening and triage. The proposed deep-learning strategy may help during future pandemics when healthcare resources are constrained relative to disease burden.
Publisher
Journal of Pure and Applied Microbiology
Published On
May 04, 2023
Authors
Madhumita Pal, Smit Parij, Ganapati Pan, Snehasish Mishra, Ranjan K Mohapatra, Kuldeep Dhama
Tags
deep learning
COVID-19
chest X-ray
VGG-16
LSTM
diagnosis
classification

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