Prediction of Nephrolithiasis Based on Extracted Features of Ct-Scan Images using Artificial Neural Networks

Abstract

Nephrolithiasis or Renal calculi or Kidney stones transpire in 1 in 10 populace at some time in their life. Renal calculi are a general cause of bloody urine and stern pain in the abdomen, flank, or groin. To locate the position, size and number of stones in the renal structure the patient is recommended to take computed tomography (CT-scan). As there is rapid raise in population the necessitate of more nephrologists is essential. So, the most important objective of this paper is to afford a supportive diagnosis system to the physician using neural networks in order to envisage nephrolithiasis based on extracted features of CT-scan. The proposed system incriminates with pre-processing, segmentation, feature extraction by applying neural network techniques and finally prediction of kidney stone is done. Artificial Neural networks are intermittently used as a dominant distinctive classifier for errands in medical diagnosis for premature detection of diseases. Here we introduced a Feed-Forward Backpropagation algorithm to lessen the diagnosis time and raise the accuracy of the system. The GLCM algorithm is introduced to extort the features which are used to train the network. The proposed system uses 22 input nodes, 10 hidden nodes and 1 output node. The network is trained until it targets its desired output. This trained network is used to cart out the classification automatically for a new model. The proposed system is tested with 50 real time samples, amid them 60% are used for training the network and 40% are used for testing the network. The intended system is implemented using MATLAB 8.5 software tool.