Comparative Study of Artificial Intelligence Detection Technology from Exception Ischemic Stroke Requiring Medical Help Imaging

<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css"> <link rel="stylesheet" href="../bootstrap.css"> <link rel="stylesheet" href="../style.css"> <link rel="icon" type="image/png" href="https://wireilla.com/w.ico" alt="logo" size="96x96"> <title>Comparative Study of Artificial Intelligence Detection Technology from Exception Ischemic Stroke Requiring Medical Help Imaging </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="Comparative Study of Artificial Intelligence Detection Technology from Exception Ischemic Stroke Requiring Medical Help Imaging"> <meta name="description" content=" Artificial intelligence is revolutionizing the interpretation of medical images, helping healthcare professionals save time on magnetic scans, CT scans and X-rays. Stroke is a global health problem, and ischemic stroke is one of the leading causes of death and disability in humans. Symptoms of an ischemic stroke appear suddenly and worsen within minutes, as most ischemic strokes occur suddenly, progress rapidly, and lead to the death of brain tissue within minutes or hours. This is why early detection of stroke is essential and remains a challenge for neurophysicists. Neurophysicists routinely use a variety of detection techniques to detect, assess and evaluate the ex-tent of premature ischemic changes in acute stroke brain imaging. Although several effective techniques exist, these methods have limitations due to unenhanced CT scans and invasive techniques. This study aims to demonstrate the limitations of certain methods, to determine detection methods by comparing the detection performance of automated and human brains. Stroke was evaluated through a literature review of recent studies.This article highlights comparative studies of different artificial intelligence (AI) techniques using medical imaging and allows the authors to orient themselves within these comparative studies, thus projecting themselves into the challenges facing artificial intelligence. "/> <meta name="keywords" content=" Artificial Intelligence, Medical Imaging, MRI, CT-scan, Ischemic Stroke , Computer Science, Technology, open access proceedings"/>   <meta name="dc.title" content="Comparative Study of Artificial Intelligence Detection Technology from Exception Ischemic Stroke Requiring Medical Help Imaging "> <meta name="citation_authors" content="Sada Anne"> <meta name="citation_authors" content="Amadou Dahirou Gueye"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="International Journal on Bioinformatics & Biosciences (IJBB) Vol.13, No.04"> <meta name="dc.date" content="2023/11/30"> <meta name="dc.identifier" content="10.5121/ijbb.2023.13401 "> <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=" Artificial intelligence is revolutionizing the interpretation of medical images, helping healthcare professionals save time on magnetic scans, CT scans and X-rays. Stroke is a global health problem, and ischemic stroke is one of the leading causes of death and disability in humans. Symptoms of an ischemic stroke appear suddenly and worsen within minutes, as most ischemic strokes occur suddenly, progress rapidly, and lead to the death of brain tissue within minutes or hours. This is why early detection of stroke is essential and remains a challenge for neurophysicists. Neurophysicists routinely use a variety of detection techniques to detect, assess and evaluate the ex-tent of premature ischemic changes in acute stroke brain imaging. Although several effective techniques exist, these methods have limitations due to unenhanced CT scans and invasive techniques. This study aims to demonstrate the limitations of certain methods, to determine detection methods by comparing the detection performance of automated and human brains. Stroke was evaluated through a literature review of recent studies.This article highlights comparative studies of different artificial intelligence (AI) techniques using medical imaging and allows the authors to orient themselves within these comparative studies, thus projecting themselves into the challenges facing artificial intelligence. "/> <meta name="dc.subject" content="Artificial Intelligence"> <meta name="dc.subject" content="Medical Imaging"> <meta name="dc.subject" content="MRI"> <meta name="dc.subject" content="CT-scan"> <meta name="dc.subject" content="Ischemic Stroke"> <meta name="dc.subject" content="Computer Science"> <meta name="dc.subject" content="Technology"> <meta name="dc.subject" content="open access proceedings">   <meta name="prism.publicationName" content="International Journal on Bioinformatics & Biosciences (IJBB) "> <meta name="prism.publicationDate" content="2023/11/30"> <meta name="prism.volume" content="13"> <meta name="prism.number" content="04"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="1">   <meta name="citation_journal_title" content="International Journal on Bioinformatics & Biosciences (IJBB) "> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_authors" content="Sada Anne and Amadou Dahirou Gueye "> <meta name="citation_title" content="Comparative Study of Artificial Intelligence Detection Technology from Exception Ischemic Stroke Requiring Medical Help Imaging"> <meta name="citation_online_date" content="2023/11/30"> <meta name="citation_issue" content="13"> <meta name="citation_firstpage" content="1"> <meta name="citation_authors" content="Sada Anne"> <meta name="citation_authors" content="Amadou Dahirou Gueye"> <meta name="citation_doi" content="10.5121/ijbb.2023.13401 "> <meta name="citation_abstract_html_url" content="https://airccse.org/journal/IJBB/current2023.html"> <meta name="citation_pdf_url" content="https://wireilla.com/papers/ijbb/V13N4/13423ijbb01.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://airccse.org/journal/IJBB/current2023.html"> <meta property="og:title" content="Comparative Study of Artificial Intelligence Detection Technology from Exception Ischemic Stroke Requiring Medical Help Imaging"> <meta property="og:description" content=" Artificial intelligence is revolutionizing the interpretation of medical images, helping healthcare professionals save time on magnetic scans, CT scans and X-rays. Stroke is a global health problem, and ischemic stroke is one of the leading causes of death and disability in humans. Symptoms of an ischemic stroke appear suddenly and worsen within minutes, as most ischemic strokes occur suddenly, progress rapidly, and lead to the death of brain tissue within minutes or hours. This is why early detection of stroke is essential and remains a challenge for neurophysicists. Neurophysicists routinely use a variety of detection techniques to detect, assess and evaluate the ex-tent of premature ischemic changes in acute stroke brain imaging. Although several effective techniques exist, these methods have limitations due to unenhanced CT scans and invasive techniques. This study aims to demonstrate the limitations of certain methods, to determine detection methods by comparing the detection performance of automated and human brains. Stroke was evaluated through a literature review of recent studies.This article highlights comparative studies of different artificial intelligence (AI) techniques using medical imaging and allows the authors to orient themselves within these comparative studies, thus projecting themselves into the challenges facing artificial intelligence. "/>   <meta name="twitter:card" content="Proceedings" /> <meta name="twitter:site" content="AIRCC" /> <meta name="twitter:title" content="Comparative Study of Artificial Intelligence Detection Technology from Exception Ischemic Stroke Requiring Medical Help Imaging" /> <meta name="twitter:description" content=" Artificial intelligence is revolutionizing the interpretation of medical images, helping healthcare professionals save time on magnetic scans, CT scans and X-rays. Stroke is a global health problem, and ischemic stroke is one of the leading causes of death and disability in humans. Symptoms of an ischemic stroke appear suddenly and worsen within minutes, as most ischemic strokes occur suddenly, progress rapidly, and lead to the death of brain tissue within minutes or hours. This is why early detection of stroke is essential and remains a challenge for neurophysicists. Neurophysicists routinely use a variety of detection techniques to detect, assess and evaluate the ex-tent of premature ischemic changes in acute stroke brain imaging. Although several effective techniques exist, these methods have limitations due to unenhanced CT scans and invasive techniques. This study aims to demonstrate the limitations of certain methods, to determine detection methods by comparing the detection performance of automated and human brains. Stroke was evaluated through a literature review of recent studies.This article highlights comparative studies of different artificial intelligence (AI) techniques using medical imaging and allows the authors to orient themselves within these comparative studies, thus projecting themselves into the challenges facing artificial intelligence. "/> <meta name="twitter:image" content="https://airccse.org/img/aircc-logo1.jpg" />  </head> <body> <nav class="navbar navbar-expand-md navbar-light bg-white fixed-top mb-3 py-1" style="opacity:0.7;"> <div class="container"> <a class="navbar-brand" href="https://wireilla.com/ijbb/index.html"><img src="../WI-simple-logo.jpg" type="image/png" alt="Brand-Logo" width="150px"; height="50px"></a> <button class="navbar-toggler" data-toggle="collapse" data-target="#navbarCollapse"><span class="navbar-toggler-icon"></span></button> <div class="collapse navbar-collapse float-text-end" id="navbarCollapse"> <ul class="navbar-nav ml-auto px-3"> <li class="nav-item"> <a class="nav-link" href="../index.html"><b>Scope & Topics</b></a> </li> <li class="nav-item"> <a class="nav-link" href="../editorial.html"><b>Editorial Board</b></a> </li> <li class="nav-item"> <a class="nav-link" href="../submission.html"><b>Paper Submission</b></a> </li> <li class="nav-item"> <a class="nav-link" href="../indexing.html"><b>Indexing</b></a> </li> <li class="nav-item"> <a class="nav-link" href="../archives.html"><b>Archives</b></a> </li> <li class="nav-item"> <a class="nav-link" href="../contact.html"><b>Contact</b></a> </li> </ul> </div> </div> </nav>  <section id="showcase"> <div id="myCarousel" class="carousel slide" data-ride="carousel"> <div class="carousel-inner"> <div class="carousel-item carousel-image-2 active"> <div class="container-fluid"> <div class="carousel-caption mb-5 d-sm-block carousel_mb" style="background-color: #495150;opacity: 0.7;border-radius: 15px;"> <h3 class=" text-white" style="padding:5px;"><b>International Journal on Bioinformatics & Biosciences (IJBB) </b></h3> <p align="center" class=" text-white"><b>ISSN : 1839-9614</b></p> </div> </div> </div> </div> </div> </section> <section id="network-design-1" class="py-5 text-dark"> <div class="container"> <div class="row"> <div class="col"> <h4 style="text-align:center">Comparative Study of Artificial Intelligence Detection Technology from Exception Ischemic Stroke Requiring Medical Help Imaging An Application of Residue Number System (RNS) to Molecular Biology </h4><br> <div class="col-md-12"> <h4>Authors</h4> </div> <div class="col-md-12"> Sada Anne and Amadou Dahirou Gueye, Alioune Diop University, Senegal </div> <br> <div class="col-md-12"> <h4>Abstract</h4> <p style="text-align:justify">Artificial intelligence is revolutionizing the interpretation of medical images, helping healthcare professionals save time on magnetic scans, CT scans and X-rays. Stroke is a global health problem, and ischemic stroke is one of the leading causes of death and disability in humans. Symptoms of an ischemic stroke appear suddenly and worsen within minutes, as most ischemic strokes occur suddenly, progress rapidly, and lead to the death of brain tissue within minutes or hours. This is why early detection of stroke is essential and remains a challenge for neurophysicists. Neurophysicists routinely use a variety of detection techniques to detect, assess and evaluate the ex-tent of premature ischemic changes in acute stroke brain imaging. Although several effective techniques exist, these methods have limitations due to unenhanced CT scans and invasive techniques. This study aims to demonstrate the limitations of certain methods, to determine detection methods by comparing the detection performance of automated and human brains. Stroke was evaluated through a literature review of recent studies.This article highlights comparative studies of different artificial intelligence (AI) techniques using medical imaging and allows the authors to orient themselves within these comparative studies, thus projecting themselves into the challenges facing artificial intelligence. </p> <h4>Keywords</h4> <p style="text-align:justify">Artificial Intelligence, Medical Imaging, MRI, CT-scan, Ischemic Stroke</p> </div> <div class="col-md-12 mb-5"> <a class="btn btn-success " style="background-color:#7c90a1" target="blank" href="http://wireilla.com/papers/ijbb/V13N4/13423ijbb01.pdf">Full Article</a> <a class="btn btn-success " style="background-color:#7c90a1" target="blank" href="../vol13.html">Volume 13</a> </div> </div> </div> </div> </section>  <section id="footer-section" class="text-white py-3 text-left"> <div class="container"> <div class="row"> <div class="card-body col-md-2"></div> <div class="card-body col-md-3 text-left"> <h6 class="mb-3"style="font-size: 15px;">About Wireilla</h6> <p><a href="../contact.html" class=" text-white">Contact Wireilla</a></p> </div> <div class="card-body follow col-md-3 text-left"> </div> <div class="card-body col-md-3 text-left "> <h6 class="mb-3 ">Home</h6> <a href="http://wireilla.com/ " target="_blank" alt="airccse logo "><img src="../WI-simple-logo.jpg " alt="client-logo " size="35x35 "></a> </div> </div> <div><p align="center">All Rights Reserved - Wireilla Scientific Publications, New South Wales, Australia</p></div> </div> </div> </section> <script src="../jquery.min.js"></script> <script src="../popper.min.js"></script> <script src="../bootstrap.min.js"></script> <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.12.4/jquery.min.js"></script> <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js"></script> </body> </html>

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Comparative Study of Artificial Intelligence Detection Technology from Exception Ischemic Stroke Requiring Medical Help Imaging