A Review on Kidney Stone Detection using ML and DL Techniques

Dr. Sheshang Degadwala; Varsha Rathva

doi:10.32628/CSEIT2410589

Authors

Dr. Sheshang Degadwala Professor & Head, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India Author
Varsha Rathva Research Scholar, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India Author

DOI:

https://doi.org/10.32628/CSEIT2410589

Keywords:

Kidney Stone Detection, Machine Learning, Deep Learning, Medical Imaging, Transfer Learning, Fine-Tuning, CT Scans

Abstract

Kidney stone detection is a critical healthcare challenge, as timely and accurate diagnosis can prevent complications. The motivation behind this review is the increasing prevalence of kidney stones and the need for more effective, non-invasive detection methods. Machine learning (ML) and deep learning (DL) techniques offer promising solutions, leveraging medical imaging data to enhance diagnostic accuracy. However, limitations such as high computational cost and reliance on large datasets hinder their full potential. The aim of this review is to analyze the latest advancements in kidney stone detection using ML and DL techniques. The objective is to compare existing methodologies, highlight their strengths and weaknesses, and suggest future research directions, particularly in integrating transfer learning and fine-tuning techniques to enhance performance.

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