• Anam Farooq
• Sucheta Sharma

Kidney disease is a standout amongst the most risky disease happening ordinarily among individuals. The chances of survival can be expanded if the tumor is identified effectively at it s beginning period. X-ray kidney imaging method is generally used to envision the life systems and structure of the brain. The pictures created by MRI are high in tissue difference and have fewer antiquities. It has a few focal points over other imaging strategies, giving high complexity between delicate tissues. Be that as it may, the measure of information is to an extreme degree a lot for manual examination, which has been one of the greatest obstructions in the viable utilization of MRI. Kidney pictures for the most part contain clamor, in-homogeneity and now and again deviation. Subsequently, exact division of kidney pictures is an exceptionally troublesome assignment. In any case, the procedure of exact division of these pictures is imperative and urgent for a right conclusion by clinical devices. To address the multifaceted nature and difficulties of the kidney MRI division issue, we initially present the essential ideas of picture division. In our proposed paper we preprocessed the image, then segmentation is performed on that image, after that classification is done by the use of KNN mechanism. The obtained results are better in term of proposed methodology is better in terms of MSE and PSNR.

MSE, PSNR, KNN, Segmentation, Preprocessing

"A NOVAL DATA MINING APPROCH FOR DETECTION OF KIDENEY DISEASE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.988-1001, June 2019, Available :http://www.jetir.org/papers/JETIR1906U33.pdf

"A NOVAL DATA MINING APPROCH FOR DETECTION OF KIDENEY DISEASE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp988-1001, June 2019, Available at : http://www.jetir.org/papers/JETIR1906U33.pdf