International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
A SURVEY ON KIDNEY STONE DETECTION USING IMAGE PROCESSING AND DEEP LEARNING Apoorva Mohite1, Aishwarya Avatade2, Meghana Galande3, Prof. Ganesh V. Madhikar4 1-3Student,
Dept. of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Maharashtra, India. 4Assistant Professor, Dept. of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract – As result of our current life style kidney stone
stones, stage horn was analysed. A commendable contribution of various researcher in the discipline of nephrolithiasis detection via means of occurring numerous algorithms to locate the kidney stone is seen. Use of neural network for the classification of urinary calculus has shown great potential.
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 preprocessed. 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 color information of the image is now reduced and converted into a single dimensional from 3 dimensional with similar patterns.
1.1 Problem Statement
has become a common health issue. There are inaccuracies in the classification of kidney stone due to the presence of noise. Also, quick and correct diagnosis of kidney stone is essential which is observed to be lacking in the currently followed practices.
Failure of Kidney can be a life changing. So that the initial detection of kidney stone is important. Kidney stones must first be identified to ensure successful surgical operations.[3]
1.2 Objective The main objective of this project is to efficiently detect kidney stone problems with the help of image, and to improve the detection rate in terms of accuracy as well as sensitivity.
Key Words: Image processing, convolution neural network, Deep learning, Machine learning, Python, CT scans.
2. LITERATURE SURVEY For this topic, many research papers have been published and many researchers have work upon it, in order to design Kidney stone detection using image processing and deep learning few of the following are discussed here. Literature survey is an information review. Which will help us in understanding and exploring concept of basis learnings So it will help us in better understanding the topics based on early information available. Literature survey is often done to connect our work with the relation of existing data.
1. INTRODUCTION The Kidney Stone issue can be seeing rising dramatically throughout the world. Shape of kidneys are like bean shaped. They are located on both side of spine behind bellies and below the ribs. Size of kidney is around size of a largest fist. Filtration of the blood is the primary function of kidneys. They maintain balance of bodily fluids by removing waste materials from it. Also, they keep electrolytes in their sufficient levels. When blood comes into kidney the work of kidney starts like removing waste and adjusting level of salt, water and minerals if it is needed. Then this filtered blood goes into body back and the waste goes into pelvis and then removed from body in the form of urine funnel shaped structure that drains down a tube known as ureter to the bladder. Each and every kidney stone having around ten percent tiny filters. They are known as nephrons. If blood stops flowing through kidney part it could be die and that can lead to a kidney failure. Formation of a stone in kidney leads to blockage of urine congenital anomalies cysts. Various types of kidney stones namely viz renal calculi stone, struvite
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