research-article

A new method for chest X-ray images categorization using transfer learning and CovidNet_2020 employing convolution neural network

Authors:

P. S. Raghavendran,

Ashok Kumar Loganathan,

Suresh Muthusamy,

Om Prava Mishra,

Ponarun Ramamoorthi,

Suma Christal Mary SundararajanAuthors Info & Claims

Soft Computing, Volume 27, Issue 19

Pages 14241 - 14251

https://doi.org/10.1007/s00500-023-08874-7

Published: 04 July 2023 Publication History

Abstract

Since computer vision has been a very emerging and happening approach in image categorization, this article describes how chest X-ray images of diverse infected and normal samples were classified using convolution neural networks, mostly under the following five categories: normal or no lung infection, COVID-19, SARS, ARDS and other pneumonia infections such as viral pneumonia, cavitating pneumonia, streptococcus pneumonia, legionella pneumonia and pneumocystis pneumonia. The proposed approach accepts the X-ray image inputs and diagnoses the lung infection under the aforementioned five categories of major pneumonia infections. Pre-trained models like VGG19, Resnet-50, and NasNetMobile are applied to the diagnosis of chest X-ray images, and their performance is compared against a newly proposed convolutional neural network called the COVNET 2020, since the network is inspired to identify the COVID-19 chest X-ray images. Transfer learning with pre-rained neural networks on massive image databases like “imagenet” has become de facto for medical image diagnosis.

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Information

Published In

cover image Soft Computing - A Fusion of Foundations, Methodologies and Applications

Soft Computing - A Fusion of Foundations, Methodologies and Applications Volume 27, Issue 19

Oct 2023

815 pages

ISSN:1432-7643

EISSN:1433-7479

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 04 July 2023

Accepted: 17 June 2023

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Cited By

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Manickam CGovindasamy MMuthusamy SParamasivam M(2024)A Novel Method for Design and Implementation of Systolic Associative Cascaded Variable Leaky Least Mean Square Adaptive Filter for Denoising of ECG SignalsWireless Personal Communications: An International Journal10.1007/s11277-024-11450-3137:2(1029-1043)Online publication date: 1-Jul-2024
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Manoharan MMuthukrishnan KBalan GArumugam SMuthusamy SRamachandran MBalodi AChinnaiyan VSekaran SGnanakkan C(2024)A Novel Method for Illegal Driver Detection and Legal Driver Identification Using Multitask Learning Based LSTM Models for Real Time ApplicationsWireless Personal Communications: An International Journal10.1007/s11277-024-11368-w136:3(1923-1944)Online publication date: 1-Jun-2024
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Gnanadesigan NLincoln GDhanasegar NMuthusamy SKannan DBalasubramanian SBacanin NSadasivuni K(2024)A New Method for Detecting the Fatigue Using Automated Deep Learning Techniques for Medical Imaging ApplicationsWireless Personal Communications: An International Journal10.1007/s11277-024-11102-6135:2(1009-1034)Online publication date: 1-Mar-2024
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Mann SYadav DMuthusamy SRathee DMishra O(2023)A Novel Method for Prediction and Analysis of COVID 19 Transmission Using Machine Learning Based Time Series ModelsWireless Personal Communications: An International Journal10.1007/s11277-023-10836-z133:3(1935-1961)Online publication date: 1-Dec-2023
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