Detection of Kidney Stone Using Machine Learning

Abstract

As result of our current life style kidney stone has become a common health issue. There are inaccuracies in the classification of kidney stone due to the presence noise. Also, quick and correct diagnosis of kidney stone is essential which is observed to be lacking in the currently followed practices. Kidney-Urine-Belly computed tomography (KUB CT) analysis is an imaging modality that has the potential to enhance kidney stone screening and diagnosis. This study explored the development of a semi-automated program that used image processing techniques and geometry principles to define the boundary, and segmentation of the kidney area, and to enhance kidney stone detection. It marked detected kidney stones and provided an output that identifies the size and location of the kidney based on pixel count. It's difficult to obtain results for large dataset using human inspection, this is where an automated kidney stone classification is implemented. The automated system uses image processing and deep learning method. The MIR and CT scan images of the proposed methodology of nephrolithiasis is pre-processed. The extraction of the key features is done using gray level concurrent matrix. The conversion of RGB format of image into gray format is essential. The colour information of the image is now reduced and converted into a single dimensional from 3 dimensional with similar patterns.