Kidney stones are the common problem in the healthcare system. It is rapidly increasing day by day and becomes a global health crisis in worldwide. Various deep learning algorithms are used for classificationof stone in kidney area. The computer aided design approach can be used for assist doctor for finding out the stone in kidney area. For kidney transplantation and dialysis, a proper treatment is required. It is important to have reliable techniques for predicting kidney stone size atits early stages. Different machine learning (ML) algorithmsare given excellent results in predicting stone. In this paper, clinicaldata is used for predicting of stone in kidney. If data have some missing values, data unbalancing problem then machine learning algorithms assist to solve this problem in which includes data preprocessing, a technique for managing missing values, data aggregation, feature extraction and prediction of result by evaluating values. In this study, deep learning algorithm for classification of kidney stone sizes automatically on the patient’s dataset is used. A total of 1000 patient’s dataset are used for finding out kidney stone size i.e., large or small. The binary classification algorithm is used for classification of stone size. We observed that our model gives best result for classification of kidney stone image size.