Neural network approach to detect renal calculi

Abstract

Back Propagation Network is the most commonly used algorithm in training neural networks. It is employed in processing the images and data to implement an automated kidney stone classification. The conventional technique for classifying medical resonance kidney images and detecting stones relies on human examination. This method is not accurate since it is impractical to handle large amount of data. Magnetic Resonance (MR) Images may inherently possess noise caused by operator errors. This causes earnest inaccuracies in classification features and diseases in image processing. However, the usage of artificial intelligent based methods along with neural networks and feature extraction has shown great potential in extracting the region of interest using back propagation network algorithms in this field. In this work, the Back Propagation Network was applied for the objective of kidney stone detection. Decision-making is carried out in two stages, Feature extraction and Image classification. The feature extraction is done using the principal component analysis and the image classification is done using Back Propagation Network (BPN). This work presents a segmentation method using the Fuzzy C-Mean (FCM) clustering algorithm. The performance of the BPN classifier was estimated in terms of training execution and classification accuracy. The Back Propagation Network gives precise classification when compared to other methods based on neural networks.