Open Access Open Access  Restricted Access Subscription Access

KIDNEY STONE DETECTION USING IMAGE PROCESSING

KAVIYARASAN C, Dr A R JayaSudha

Abstract


Kidney stones have increased in prevalence in recent years, and early detection is essential since failure to do so may lead to problems and the need for surgical removal of the stone. Because image processing has a bias towards producing exact findings and is an automatically scalable approach of stone detection, it opens the way for accurate stone detection. Due to their size and location, kidney stones might be difficult to see with ultrasonography.


Full Text:

PDF

References


Linta Antony, Sami Azam'' A Comprehensive Unsupervised Framework For Chronic Kidney Disease Prediction'', VOL. 9, Sep 2021

M.Akshaya, R.Nithusaa ''Kidney stone Detection Using Neural Networks'', Dec 2020

A. V. Ravindra, N. Sriraam, and M. Geetha, ‘‘Classification of non-chronic and chronic kidney disease using SVM neural networks,’’ Int. J. Eng. Technol., vol.7, no. 1, pp. 191–194, 2018.

S. Y. Yashfi, M. A. Islam, Pritilata, N. Sakib, T. Islam, M. Shahbaaz, and S. S. Pantho, ‘‘Risk prediction of chronic kidney disease using machine learning

algorithms,’’ in Proc. 11th Int. Conf. Comput., Commun. Netw. Technol. (ICCCNT), Jul. 2020, pp. 1–5.

D. Jain and V. Singh, ‘‘Feature selection and classification systems for chronic disease prediction: A review,’’ Egyptian Informat. J., vol. 19, no. 3, pp. 179–189, Nov. 2018, doi: 10.1016/j.eij.2018.03.002.

P. T. Akkasaligar and S. Biradar, ''Classification of medical ultrasound images of kidney'', 2nd International Conference on Computing for Sustainable Global

Development (INDIACom), IEEE 2014, pp. 1914-1918.


Refbacks

  • There are currently no refbacks.