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  <doc>
    <id>52</id>
    <completedYear>2018</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>76</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>bachelorthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mapping of natural learning processes for the development of neural network architectures - Implementation and comparison of approaches in the classification of radiological data sets</title>
    <abstract language="eng">In order to provide more transparency on convolutional neural networks (CNN) for education and research purposes this work has three main objectives: &#13;
&#13;
• Create CNN models based on two different architectures, which classify magnetic resonance images of the brain into normal and abnormal&#13;
&#13;
• Investigate these models by applying state of the art visualization techniques. For this purpose, a simple accessible Application Interface (API) for Keras sequential models will be developed.&#13;
&#13;
• Describe the quality of the models based on the visualizations and compare their overall classification to the human classification procedure.</abstract>
    <additionalTitle language="deu">Abbildung natürlicher Lernprozesse für die Entwicklung neuronaler Netzarchitekturen - Implementierung und Vergleich von Ansätzen bei der Klassifizierung eines radiologischen Datensatzes</additionalTitle>
    <identifier type="urn">urn:nbn:de:bvb:860-opus4-521</identifier>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Creative Commons Lizenz (es gilt das deutsche Urheberrecht)</licence>
    <author>Jakob Frederik Dexl</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CNN</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>visualization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>radiology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>image classification</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Neuronales Netz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Visualisierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Radiologie</value>
    </subject>
    <collection role="institutes" number="">Fakultät Elektrotechnik und Wirtschaftsingenieurwesen</collection>
    <thesisPublisher>Hochschule für Angewandte Wissenschaften Landshut</thesisPublisher>
    <thesisGrantor>Hochschule für Angewandte Wissenschaften Landshut</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-haw-landshut/files/52/0_Bachelorthesis_Jakob_Dexl_2018.pdf</file>
  </doc>
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