Kidney stone classification using deep learning neural network

Vasudeva, Nisha; Sharma, Vivek Kumar; Sharma, Shashi; Sharma, Ravi Shankar; Sharma, Satyajeet; Sharma, Gajanand

doi:10.47974/JDMSC-1762

Open Access Research Article

Kidney stone classification using deep learning neural network

Nisha Vasudevavasudeva.nisha1@gmail.comSchool of Computing Science and Engineering Galgotias UniversityGreater Noida, Uttar Pradesh, 522302, IndiaView full profile → , Vivek Kumar Sharmavkshere4every1@gmail.comSchool of Computing Science and Engineering Galgotias UniversityGreater Noida, Uttar Pradesh, 302022, IndiaView full profile → , Shashi Sharmapathak.shashi26@gmail.comDepartment of Computer Science Engineering JECRC UniversityJaipur, Rajasthan, IndiaView full profile → , Ravi Shankar Sharmaer.ravishankarsharma@gmail.comDepartment of Computer Science Engineering JECRC UniversityDepartment of Computer Science & Engineering JECRC UniversityJaipur, Rajasthan, 303905, IndiaView full profile → , Satyajeet Sharmasharma.satyajeet24@gmail.comDepartment of Computer Science Engineering JECRC UniversityDepartment of Computer Science & Engineering JECRC UniversityJaipur, Rajasthan, 303905, IndiaView full profile → , ^*Gajanand SharmaCorresponding authorgajanan.sharma@gmail.comDepartment of Computer Science Engineering JECRC UniversityDepartment of Computer Science & Engineering JECRC UniversityJaipur, Rajasthan, 303905, IndiaView full profile →

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pp. 1393–1401Vol. 26Issue 5August 2023DOI: 10.47974/JDMSC-1762 Crossmark XML

Published Online:: 09 Sep 2023
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-1762
Pages:: 1393–1401

Abstract

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.

Keywords

Deep learning Kidney stone Classification

Subject Classifications

Primary 68T07Secondary 03C45

References

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