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                <text>Conference Papers</text>
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    <name>Conference Paper</name>
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              <text>Development and Evaluation of an Artificial Intelligence-Based System for Pancreatic Cancer Detection and Diagnosis</text>
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          <name>Subject</name>
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              <text>Artificial intelligence; Deep learning; Machine learning; Pancreatic cancer; Validation studies</text>
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              <text>Due to its aggressive nature and late-stage manifestation, pancreatic cancer is a difficult illness to find and diagnose. The creation of a pancreatic cancer detection and diagnosis system based on artificial intelligence (AI) has the potential to increase early detection and improve treatment results. We have described the creation and assessment of an AI-based system in this paper that is intended for the identification of pancreatic cancer. A large dataset including a variety of medical pictures, including CT scans, MRI scans, and PET scans, as well as the related clinical information, was gathered for the study. With the help of the annotated dataset, a deep learning model built on convolutional neural networks was created. The proposed AI-based solution was then assessed using a separate test dataset made up of control cases and known pancreatic cancer patients. A significant effectiveness for the early diagnosis of the disease was shown by the systems excellent precision as well as sensitivity in identifying pancreatic tumors. The outcomes of this investigation demonstrate the promise of AI-based systems for pancreatic cancer detection and diagnosis.  2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.</text>
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          <name>Creator</name>
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              <text>Upreti K.; Mittal S.; Vats P.; Haque M.; Pawar V.; Haque M.</text>
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              <text>Communications in Computer and Information Science, Vol-1920, pp. 26-38.</text>
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          <name>Publisher</name>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-3-031-45121-8_3" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-3-031-45121-8_3&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175999491&amp;amp;doi=10.1007%2F978-3-031-45121-8_3&amp;amp;partnerID=40&amp;amp;md5=f6f4acbfaf88c94269a1fed2a86d5cca" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175999491&amp;amp;doi=10.1007%2f978-3-031-45121-8_3&amp;amp;partnerID=40&amp;amp;md5=f6f4acbfaf88c94269a1fed2a86d5cca&lt;/a&gt;</text>
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              <text>Restricted Access</text>
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              <text>ISSN: 18650929; ISBN: 978-303145120-1</text>
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              <text>Online</text>
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              <text>Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Mittal S., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Vats P., SCSE, Manipal University Jaipur, Rajasthan, India; Haque M., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Patil Vidyapeeth (Deemed to be University), Pune, India; Pawar V., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Patil Vidyapeeth (Deemed to be University), Pune, India; Haque M., National Medical College and Teaching Hospital, Birganj, Nepal</text>
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