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

K. Viswanath, Dr. R. Gunasundari


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


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

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