Concatenation Technique in Convolutional Neural Networks for COVID-19 Detection Based on X-ray Images

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <title>Concatenation Technique in Convolutional Neural Networks for COVID-19 Detection Based on X-ray Images</title>  <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <meta name="title" content="Concatenation Technique in Convolutional Neural Networks for COVID-19 Detection Based on X-ray Images"> <meta name="description" content="In this paper we present a Convolutional Neural Network consisting of NASNet and MobileNet in parallel (concatenation) to classify three classes COVID-19, normal and pneumonia, depending on a dataset of 1083 x-ray images divided into 361 images for each class. VGG16 and RESNet152-v2 models were also prepared and trained on the same dataset to compare performance of the proposed model with their performance. After training the networks and evaluating their performance, an overall accuracy of 96.91%for the proposed model, 92.59% for VGG16 model and 94.14% for RESNet152. We obtained accuracy, sensitivity, specificity and precision of 99.69%, 99.07%, 100% and 100% respectively for the proposed model related to the COVID-19 class. These results were better than the results of other models. The conclusion, neural networks are built from models in parallel are most effective when the data available for training are small and the features of different classes are similar"/> <meta name="keywords" content="Deep Learning, Concatenation Technique, Convolutional Neural Networks, COVID-19, Transfer Learning."/>   <meta name="dc.title" content="Concatenation Technique in Convolutional Neural Networks for COVID-19 Detection Based on X-ray Images"> <meta name="citation_author" content="Yakoop Razzaz Hamoud Qasim"> <meta name="citation_author" content="Habeb Abdulkhaleq Mohammed Hassan"> <meta name="citation_author" content="Abdulelah Abdulkhaleq Mohammed Hassan"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="Computer Science & Information Technology (CS & IT), Vol 10, No.16"> <meta name="dc.date" content="28-11-2020"> <meta name="dc.identifier" content="10.5121/csit.2020.101602"> <meta name="dc.publisher" content="AIRCC Publishing Corporation"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf"> <meta name="dc.language" content="en"> <meta name="dc.description" content="In this paper we present a Convolutional Neural Network consisting of NASNet and MobileNet in parallel (concatenation) to classify three classes COVID-19, normal and pneumonia, depending on a dataset of 1083 x-ray images divided into 361 images for each class. VGG16 and RESNet152-v2 models were also prepared and trained on the same dataset to compare performance of the proposed model with their performance. After training the networks and evaluating their performance, an overall accuracy of 96.91%for the proposed model, 92.59% for VGG16 model and 94.14% for RESNet152. We obtained accuracy, sensitivity, specificity and precision of 99.69%, 99.07%, 100% and 100% respectively for the proposed model related to the COVID-19 class. These results were better than the results of other models. The conclusion, neural networks are built from models in parallel are most effective when the data available for training are small and the features of different classes are similar"/> <meta name="dc.subject" content="Deep Learning"> <meta name="dc.subject" content="Transfer Learning."/> <meta name="dc.subject" content="Concatenation Technique"> <meta name="dc.subject" content="Clustering"> <meta name="dc.subject" content="Convolutional Neural Networks"> <meta name="dc.subject" content="COVID-19,">   <meta name="prism.publicationName" content="Computer Science & Information Technology (CS & IT)"> <meta name="prism.publicationDate" content="28-11-2020"> <meta name="prism.volume" content="10"> <meta name="prism.number" content="16"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="17">   <meta name="citation_journal_title" content="Computer Science & Information Technology (CS & IT)"> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_author" content="Yakoop Razzaz Hamoud Qasim"> <meta name="citation_author" content="Habeb Abdulkhaleq Mohammed Hassan"> <meta name="citation_author" content="Abdulelah Abdulkhaleq Mohammed Hassan"> <meta name="citation_title" content="Concatenation Technique in Convolutional Neural Networks for COVID-19 Detection Based on X-ray Images"> <meta name="citation_online_date" content="28-11-2020"> <meta name="citation_volume" content="10"> <meta name="citation_issue" content="16"> <meta name="citation_firstpage" content="17"> <meta name="citation_author" content="Yakoop Razzaz Hamoud Qasim"> <meta name="citation_author" content="Habeb Abdulkhaleq Mohammed Hassan"> <meta name="citation_author" content="Abdulelah Abdulkhaleq Mohammed Hassan"> <meta name="citation_doi" content="10.5121/csit.2020.101602"> <meta name="citation_abstract_html_url" content="https://aircconline.com/csit/abstract/v10n16/csit101602.html"> <meta name="citation_pdf_url" content="https://aircconline.com/csit/papers/vol10/csit101602.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://aircconline.com/csit/abstract/v10n16/csit101602.html"/> <meta property="og:title" content="Concatenation Technique in Convolutional Neural Networks for COVID-19 Detection Based on X-ray Images"> <meta property="og:description" content="In this paper we present a Convolutional Neural Network consisting of NASNet and MobileNet in parallel (concatenation) to classify three classes COVID-19, normal and pneumonia, depending on a dataset of 1083 x-ray images divided into 361 images for each class. VGG16 and RESNet152-v2 models were also prepared and trained on the same dataset to compare performance of the proposed model with their performance. After training the networks and evaluating their performance, an overall accuracy of 96.91%for the proposed model, 92.59% for VGG16 model and 94.14% for RESNet152. We obtained accuracy, sensitivity, specificity and precision of 99.69%, 99.07%, 100% and 100% respectively for the proposed model related to the COVID-19 class. These results were better than the results of other models. The conclusion, neural networks are built from models in parallel are most effective when the data available for training are small and the features of different classes are similar"/> <meta name="keywords" content="Deep Learning, Concatenation Technique, Convolutional Neural Networks, COVID-19, Transfer Learning."/>   <meta name="google-site-verification" content="t8rHIcM8EfjIqfQzQ0IdYIiA9JxDD0uUZAitBCzsOIw" /> <meta name="yandex-verification" content="e3d2d5a32c7241f4" />  <link rel="icon" type="image/ico" href="../img/ico.ico"/> <link rel="stylesheet" type="text/css" href="../main1.css" media="screen" /> <style type="text/css"> a{ color:white; text-decoration:none; line-height:20px; } ul li a{ font-weight:bold; color:#000; list-style:none; text-decoration:none; size:10px;} .imagess { height:90px; text-align:left; margin:0px 5px 2px 8px; float:right; border:none; } #left p { font-family: CALIBRI; font-size: 16px; margin-left: 20px; font-weight: 500; } .right { margin-right: 20px; } #button{ float: left; font-size: 14px; margin-left: 10px; height: 28px; width: auto; background-color: #1e86c6; } </style> </head> <body> <div class="font"> <div id="wap"> <div id="page"> <div id="top"> <form action="https://airccj.org/csecfp/library/Search.php" method="get" target="_blank" > <table width="100%" cellspacing="0" cellpadding="0" > <tr class="search_input"> <td width="665" align="right"> </td> <td width="236" > <input name="title" type="text" value="Enter the paper title" class="search_textbox" onclick="if(this.value=='Enter the paper title'){this.value=''}" onblur="if(this.value==''){this.value='Enter the paper title'}" /> </td> <td width="59"> <input type="image" src="../img/go.gif" /> </td> </tr> <tr> <td colspan="3" valign="top"><img src="../img/top1.gif" alt="Academy & Industry Research Collaboration Center (AIRCC)" /></td> </tr> </table> </form> </div> <div id="font-face"> <div id="menu"> <a href="http://airccse.org">Home</a> <a href="http://airccse.org/journal.html">Journals</a> <a href="http://airccse.org/ethics.html">Ethics</a> <a href="http://airccse.org/conference.html">Conferences</a> <a href="http://airccse.org/past.html">Past Events</a> <a href="http://airccse.org/b.html">Submission</a> </div> <div id="content"> <div id="left"> <h2 class="lighter"><font size="2">Volume 10, Number 16, November 2020</font></h2> <h4 style="text-align:center;height:auto;"><a>Concatenation Technique in Convolutional Neural Networks for COVID-19 Detection Based on X-ray Images</a></h4> <h3>  Authors</h3> <p class="#left right" style="text-align:">Yakoop Razzaz Hamoud Qasim, Habeb Abdulkhaleq Mohammed Hassan, Abdulelah Abdulkhaleq Mohammed Hassan, Taiz University, Yemen</p> <h3>  Abstract</h3> <p class="#left right" style="text-align:justify">In this paper we present a Convolutional Neural Network consisting of NASNet and MobileNet in parallel (concatenation) to classify three classes COVID-19, normal and pneumonia, depending on a dataset of 1083 x-ray images divided into 361 images for each class. VGG16 and RESNet152-v2 models were also prepared and trained on the same dataset to compare performance of the proposed model with their performance. After training the networks and evaluating their performance, an overall accuracy of 96.91%for the proposed model, 92.59% for VGG16 model and 94.14% for RESNet152. We obtained accuracy, sensitivity, specificity and precision of 99.69%, 99.07%, 100% and 100% respectively for the proposed model related to the COVID-19 class. These results were better than the results of other models. The conclusion, neural networks are built from models in parallel are most effective when the data available for training are small and the features of different classes are similar. </p> <h3>  Keywords</h3> <p class="#left right" style="text-align:justify">Deep Learning, Concatenation Technique, Convolutional Neural Networks, COVID-19, Transfer Learning.</p><br> <button type="button" id="button"><a target="blank" href="/csit/papers/vol10/csit101602.pdf">Full Text</a></button>   <button type="button" id="button"><a href="http://airccse.org/csit/V10N16.html">Volume 10, Number 16</a></button> <br><br><br><br><br> </div> <div id="right"> <div class="menu_right"> <ul> <li id="id"><a href="http://airccse.org/editorial.html">Editorial Board</a></li> <li><a href="http://airccse.org/arch.html">Archives</a></li> <li><a href="http://airccse.org/indexing.html">Indexing</a></li> <li><a href="http://airccse.org/faq.html" target="_blank">FAQ</a></li> </ul> </div> <div class="clear_left"></div> <br> </div> <div class="clear"></div> <div id="footer"> <table width="100%" > <tr> <td width="46%" class="F_menu"><a href="http://airccse.org/subscription.html">Subscription</a> <a href="http://airccse.org/membership.html">Membership</a> <a href="http://airccse.org/cscp.html">AIRCC CSCP</a> <a href="http://airccse.org/acontact.html">Contact Us</a> </td> <td width="54%" align="right"><a href="http://airccse.org/index.php"><img src="/csit/abstract/img/logo.gif" alt="" width="21" height="24" /></a><a href="http://www.facebook.com/AIRCCSE"><img src="/csit/abstract/img/facebook.jpeg" alt="" width="21" height="24" /></a><a href="https://twitter.com/AIRCCFP"><img src="/csit/abstract/img/twitter.jpeg" alt="" width="21" height="24" /></a><a href="http://cfptech.wordpress.com/"><img src="/csit/abstract/img/index1.jpeg" alt="" width="21" height="24" /></a></td> </tr> <tr><td height="25" colspan="2"> <p align="center">All Rights Reserved ® AIRCC</p> </td></tr> </table> </div> </div> </div> </div> </div> </div> </body> </html>

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Concatenation Technique in Convolutional Neural Networks for COVID-19 Detection Based on X-ray Images