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An Investigation of AI Techniques for Detecting Kidney Stones in CT Scan Images Through Advanced Image Processing
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Author(s): Ranjit Barua (CHST, IIEST Shibpur, India)
Copyright: 2024
Pages: 18
Source title:
Enhancing Medical Imaging with Emerging Technologies
Source Author(s)/Editor(s): Avinash Kumar Sharma (Sharda University, India), Nitin Chanderwal (University of Cincinnati, USA), Shobhit Tyagi (Sharda University, India), Prashant Upadhyay (Sharda University, India)and Amit Kumar Tyagi (National Institute of Fashion Technology, New Delhi, India)
DOI: 10.4018/979-8-3693-5261-8.ch008
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Abstract
Image processing techniques provide an automated and objective way to detect kidney stones in medical images, reducing the need for manual interpretation and potentially improving the accuracy and efficiency of diagnosis. It's important to note that the specific algorithms and methods used can vary depending on the type of medical imaging and the equipment employed for image acquisition. Kidney stone disease is increasingly prevalent today, primarily caused by the high concentration of minerals and salts in urine, resulting in the formation of hard deposits known as kidney stones. The gold standard for kidney stone diagnosis has shifted to computed tomography (CT). In this chapter, the authors present a concise overview of recent advancements in the diagnosis of kidney stones utilizing image processing techniques.
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