KIDNEY NEPHROLITHIASIS DETECTION AND CLASSIFICATION IN COMPUTED TOMOGRAPHY IMAGES

ELECTRONICS AND COMMUNICATION ENGINEERING

As the research of the Kidney International Research center shows that the population of the chronic kidney disease (CKD) is beyond 3.8 hundred million and still increase rapidly. Kidney stone problem also called as nephrolithiasis is a common type of urological disease with a high recurrence rate of 20 % after one year, 50 % over a period of 5-10 years and 75 % over a period of 20 years. Over the last 20 years (1999-2019),the prevalence rate of kidney nephrolithiasis disease in India has increased nearly twice from 5.95 % to 10.63 % which has been documented in India. Kidney stone disease is a progressive disease that damage the kidneys leading to be permanent damage and undone problem. Some Low level classification techniques are not efficient due to their low accuracy level. Therefore, this project plays a vital role to identify and to cure kidney stone disease before the permanent damage is done with efficient techniques with high accuracy results. This project propose a high efficiency classification framework to detect and to classify the kidney nephrolithiasis in Computed Tomography images using machine and deep learning classifiers. To segment and to extract the features of detected kidney nephrolithiasis with the size of stone using clustering algorithm.

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