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<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>IJIRCSTJournal</PublisherName>
      <JournalTitle>International Journal of Innovative Research in Computer Science and Technology</JournalTitle>
      <PISSN>I</PISSN>
      <EISSN>S</EISSN>
      <Volume-Issue>Volume 10 Issue 5</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Computer Science &amp; Engineering</IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>September - October 2022</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2022</Year>
        <Month>09</Month>
        <Day>01</Day>
      </PubDate>
      <ArticleType>Computer Sciences</ArticleType>
      <ArticleTitle>Kidney Tumour Detection Using Deep Neural Network</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>5</FirstPage>
      <LastPage>12</LastPage>
      <AuthorList>
        <Author>
          <FirstName>Tawseeful Haziq</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>Y</CorrespondingAuthor>
          <ORCID/>
                      <FirstName>Ashish Obroi</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
                    <FirstName>Yogesh</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
           
        </Author>
      </AuthorList>
      <DOI>https://doi.org/10.55524/ijircst.2022.10.5.2</DOI>
      <Abstract>Classifying the malignancy of a renal tumour is one of the most important urological duties because it plays a key role in determining whether or not to undergo kidney removal surgery (nephrectomy). Currently, the radiological diagnostic made us89++ing computed tomography (CT) scans determines the likelihood of a tumour being malignant. However, it&amp;#39;s believed that up to 16 percent of nephrectomies may have been avoided since a postoperative histological study revealed that a tumour that had been first identified as malignant was actually benign. Numerous false-positive diagnoses lead to unnecessary nephrectomies, which increase the chance of post-procedural problems. In this article, we offer a computer-aided diagnostic method that analyses a CT scan to determine the tumour&amp;rsquo;s malignancy. The prediction, which is used to identify false-positive diagnoses, is carried out following radiological diagnosis. Our solution can complete this challenge with an F1 score of 0.84. Additionally, we suggest a cutting-edge method for knowledge transmission in the medical field using colorization-based pre-processing, which can raise the F1-score by as much as to 1.8.</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords>Deep neural, Renal tumour, CT-Scan, Benign, Malignant</Keywords>
      <URLs>
        <Abstract>https://ijircst.org/abstract.php?article_id=1026</Abstract>
      </URLs>      
    </Journal>
  </Article>
</ArticleSet>