ANALYSIS THE EFFECTIVENESS OF NOVEL DEVELOPMENT OF PATIENT ASSISTIVE TOOL FOR DETECTION OF ABNORMALITIES IN KIDNEY USING SVM & ANN CLASSIFICATION TECHNIQUES

Kidney stone disease is one of the major life threatening ailments persisting worldwide. The stone diseases remain unnoticed in the initial stage, which in turn damages the kidney as they develop. A majority of people are affected by kidney failure due to diabetes mellitus, hypertension, and so forth. Since kidney malfunctioning can be menacing, diagnosis of the problem in the initial stages is advisable. The detection of kidney stones using ultrasound imaging is a highly challenging task as they are of low contrast and contain speckle noise. This challenge is overcome by employing suitable image processing techniques. The ultrasound image is first pre-processed to get rid of speckle noise using the image restoration process. The restored image is smoothened using Gabor filter and the subsequent image is enhanced by histogram equalization .The pre-processed image is achieved with level set segmentation to detect the stone region. Segmentation process is employed twice for getting better results; first to segment kidney portion and then to segment the stone portion respectively. Data mining techniques plays a vital role in different domains such as text mining, graph mining, medical mining, multimedia mining and web mining. The objective of this work is to predict kidney Stone diseases by using Support Vector Machine (SVM) and Artificial Neural Network (ANN). The aim of this work is to analysis the performance of these two algorithms on the basis of its accuracy and execution time.