• Anam Farooq
• Sucheta Sharma

Digital chronic kidney images processing provides graphical mode for detection and prevention of diseases with the collaboration of machine learning. Machine learning contains legion of mechanisms that can work upon the feature extracted from the chronic kidney images. This paper performs analysis of techniques associated with machine learning such as SVM, Regression analysis, random forest and MSVM. In addition detailed procedure followed for classification of MRI chronic kidney images for chronic kidney detection. Parameters considered for evaluation in each research is also discussed in this paper. Comparative analysis of various techniques can be used to choose best possible technique for future endeavours.

Digital chronic kidney images processing, SVM, Regression analysis, random forest, MSVM

"REVIEW OF TECHNQIUES USED TO DETECT ABNORMALITIES FROM MRI CHRONIC KIDNEY IMAGESS USING DATA MINING APPROCHES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.792-798, June 2019, Available :http://www.jetir.org/papers/JETIR1906K97.pdf

"REVIEW OF TECHNQIUES USED TO DETECT ABNORMALITIES FROM MRI CHRONIC KIDNEY IMAGESS USING DATA MINING APPROCHES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp792-798, June 2019, Available at : http://www.jetir.org/papers/JETIR1906K97.pdf