The kidney is an important organ of humans for balancing fluid levels, removing waste and cleaning the blood. The primary function of the kidney is to maintain blood pressure normal with acid balance. Due to the modern life cycle and food habits, stones are developed in the kidney.Persons who are susceptible to kidney stones have a higher risk of other kidney complications. Different techniques have been followed to diagnose stones in the kidney. Among these, image-processing based diagnose offer the best solution in terms of accuracy. But, the varied structure of stones makes the detection complex. So, more effective computer-aided models are essential to support health care persons in making accurate conclusions. This work reviews image processing steps, preprocessing filters,and previously proposed machine learning and deep learning models for the detection of kidney stones. Also, the performance metric for evaluation and data set details for further processing are discussed.