Kidney Stone Detection using CNN Algorithm

Nisha N; Shrieya S; R Shreyas; Keerti Kulkarni; Jyoti Raghavendra Munavalli

doi:10.32628/CSEIT241061204

Authors

Nisha N Department of Electronics & Communication Engineering, BNM Institute of Technology, Bengaluru, Karnataka, India Author
Shrieya S Department of Electronics & Communication Engineering, BNM Institute of Technology, Bengaluru, Karnataka, India Author
R Shreyas Department of Electronics & Communication Engineering, BNM Institute of Technology, Bengaluru, Karnataka, India Author
Keerti Kulkarni Department of Electronics & Communication Engineering, BNM Institute of Technology, Bengaluru, Karnataka, India Author
Jyoti Raghavendra Munavalli Department of Electronics & Communication Engineering, BNM Institute of Technology, Bengaluru, Karnataka, India Author

DOI:

https://doi.org/10.32628/CSEIT241061204

Keywords:

Kidney Stone Detection, Convolutional Neural Networks, medical imaging, diagnostic accuracy, automated analysis, model training

Abstract

Kidney stones are a prevalent condition that can cause intense pain, with early detection being critical for effective treatment. Traditionally, radiologists rely on CT scans or X-rays to identify kidney stones, but this manual process can be time-consuming and subject to human error. Convolutional Neural Networks (CNNs) are emerging as a valuable tool in medical imaging, offering the potential to automate the kidney stone detection process and improve diagnostic accuracy. By training on extensive datasets of kidney images, CNNs can learn to detect stones and reduce inconsistencies between different radiologists' analyses. The CNN model is trained by processing labeled images iteratively, adjusting its internal parameters to improve detection accuracy, and is evaluated using metrics like precision, recall, and accuracy. Though challenges remain, including data acquisition, potential model bias, and regulatory considerations, CNNs could transform kidney stone diagnostics, enabling faster, more reliable diagnoses and supporting radiologists in delivering improved patient care outcomes.

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