Abstract
On some days, kidney stones can become a big problem and if not detected early, then it will cause complications and sometimes surgery is in addition to what is needed to discover the stone. Here, to see a stone that is visible very well, credits to the image processing because by processing the image there is a bent to promote accurate and automatic results on how to find a stone. Due to noise, the separation of kidney stones is generally inaccurate. Kidney stones have become more common in recent years as a result of a variety of causes. Using human and operational tests to generate findings for huge databases is tough. Identifying the precise position of the kidney stone during surgery is quite difficult. Kidney stone disease is one of the world's most dangerous diseases. Digital image and data processing methods are used to build an automated kidney stone identification. On CT scans and MRIs, there is a lot of noise, which adds to low accuracy. Neural network-based artificial intelligence approaches have produced impressive outcomes. Artificial intelligence, such as neural networks, has shown to be quite useful in this field. As a result, the Digital Image Processing is being used in this manuscript.
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Data availability
The dataset that supports the findings of this study is available in the public domain at the link https://github.com/muhammedtalo/Kidney_stone_detection.
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Saini, R.K., Saini, H. & Singh, H. Enhanced Kidney Stone Detections Using Digital Image Processing Techniques. SN COMPUT. SCI. 5, 790 (2024). https://doi.org/10.1007/s42979-024-03133-4
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DOI: https://doi.org/10.1007/s42979-024-03133-4