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              <text>A Novel Preprocessing Technique to Aid the Detection of Infected Areas of CT Images in COVID-19 Patients Artificial Intelligence (AI) for Communication Systems</text>
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              <text>An innovative preprocessing method for discerning infected areas in CT images of COVID-19 is described in this abstract. The methodology being suggested exploits the capabilities of artificial intelligence (AI) to improve disease detection communication systems. By employing sophisticated AI algorithms to preprocess CT images, the method seeks to increase the precision and effectiveness of COVID-19-associated area detection. The incorporation of artificial intelligence (AI) into communication systems facilitates enhanced image analysis, resulting in improved diagnostic capabilities and treatment strategizing. The study's findings demonstrate the potential of preprocessing techniques powered by artificial intelligence in augmenting communication systems with the aim of enhancing healthcare outcomes.   2024 IEEE.</text>
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              <text>Kalaivani J.; Arunachalam A.S.; Gobinath R.</text>
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              <text>TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/TQCEBT59414.2024.10545304" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/TQCEBT59414.2024.10545304&lt;/a&gt;
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              <text>ISBN: 979-835038427-7</text>
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              <text>Kalaivani J., Vels Institute of Science, Technology and Advanced Studies (VISTAS), School of Computing Sciences, Department of Computer Science, Chennai, India; Arunachalam A.S., Vels Institute of Science, Technology and Advanced Studies (VISTAS), School of Computing Sciences, Department of Computer Science, Chennai, India; Gobinath R., Christ University, Department of Computer Science, Bengaluru, India</text>
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