Kidney Stone Detection Using Image Processing and Neural Networks

Abstract

An automated kidney stone classification is implemented using a Back Propagation Network (BPN) and image and data processing techniques. There are inaccuracies in the classification of kidney stones due to the presence of noise. Kidney stones have become more common problem in recent years due to a variety of factors. It is difficult to obtain results for large datasets using human inspection and operators. There is a lot of noise in CT scans and MRI’s, which contributes to inaccuracies. Artificial intelligence approaches based on neural networks have shown impressive results. Artificial intelligence methods, such as neural networks, have proven to be extremely useful in this area. As a result, the Back-Propagation Network (BPN) is being used in this project. GLCM is used to extract features, which are then classified using BPN. The Fuzzy C-Mean (FCM) clustering algorithm is used in this project to segment computed tomography images in order to detect kidney stones in their early stages.