Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Low Power VLSI based Design and Implementation of MLP-BP Neural Network for Detection of Kidney Stone in Real Time


Affiliations
1 Pondicherry Engineering College, India
2 Dept of ECE, Pondicherry Engineering College, Pondicherry, India
     

   Subscribe/Renew Journal


In recent years one of the most common problems that occur in the human urinary system is kidney stones or urinary stones. There are many Methods of medical imaging that can be used to examine parameters of human kidneys, for example magnetic resonance imaging (MRI), x-ray computed tomography (CT), ultrasound imaging (US), and many others. This detection is very important for the doctor to determine the status of the kidneys and also to visualize any abnormalities present in the kidney [2]. Any person affected with a problem in kidney suffers with a pain in early stage. The detection of abnormalities of kidney inside the body is a main field of study in medical research by bio-medical image processing[1-4], Due to some abnormalities (speckle noise) in ultrasound or MRI images and artifacts, wrong diagnosis may happen by analyzing the scanned image. Therefore in this work the main focus is on development of new hardware implementation based on neural network architecture for detection of kidney stone in real time by optimizing area, power and speed on FPGA [5]. This algorithm implemented on Vertex-II Pro FPGA device and simulated in matlab [9].


Keywords

Kidney Stone Disease, Multilayer Perceptions, Back Propagation, Artificial Neural Networks, Training Algorithm, Verilog, Montgomery Multiplier and Carry Save Adder.
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 209

PDF Views: 3




  • Low Power VLSI based Design and Implementation of MLP-BP Neural Network for Detection of Kidney Stone in Real Time

Abstract Views: 209  |  PDF Views: 3

Authors

K. Viswanath
Pondicherry Engineering College, India
R. Gunasundari
Dept of ECE, Pondicherry Engineering College, Pondicherry, India

Abstract


In recent years one of the most common problems that occur in the human urinary system is kidney stones or urinary stones. There are many Methods of medical imaging that can be used to examine parameters of human kidneys, for example magnetic resonance imaging (MRI), x-ray computed tomography (CT), ultrasound imaging (US), and many others. This detection is very important for the doctor to determine the status of the kidneys and also to visualize any abnormalities present in the kidney [2]. Any person affected with a problem in kidney suffers with a pain in early stage. The detection of abnormalities of kidney inside the body is a main field of study in medical research by bio-medical image processing[1-4], Due to some abnormalities (speckle noise) in ultrasound or MRI images and artifacts, wrong diagnosis may happen by analyzing the scanned image. Therefore in this work the main focus is on development of new hardware implementation based on neural network architecture for detection of kidney stone in real time by optimizing area, power and speed on FPGA [5]. This algorithm implemented on Vertex-II Pro FPGA device and simulated in matlab [9].


Keywords


Kidney Stone Disease, Multilayer Perceptions, Back Propagation, Artificial Neural Networks, Training Algorithm, Verilog, Montgomery Multiplier and Carry Save Adder.



DOI: https://doi.org/10.36039/ciitaas%2F5%2F4%2F2013%2F106855.175-180