Affiliations: Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India
Correspondence:
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Corresponding author: R. Deiva Nayagam, Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India. E-mail:deivam90@gmail.com
Abstract: This work intends to an integration of implementing an automated
diagnostic systems for breast cancer detection using Artificial Neural
Network (ANN) in FPGA. In the world, breast cancer is the fifth most common
cause of cancer death. So better classification system is needed for
diagnosing breast cancer disease. In this work, the training and testing of
the Multilayer Perceptron Neural Network (MLPNN) with Back Propagation
Network (BPN) is done with the attributes of the record of the Wisconsin
Breast Cancer Database (WBCD). The neural network lacks the flexibility
during off line training. In order to overcome the flexibility, it is
necessary to train and test the network on on-chip neural network using
FPGA. The purpose is to determine the cancer of patients either having
benign or malignant through an FPGA based implementation of smart
instrument. In order to implement the hardware, VERILOG coding is done for
ANN and synthesized by Xilinx family XC5VLX50TFFT1136 FPGA Virtex 5 board
using XILINX ISE tool to get the netlist of ANN. Finally the netlist is
mapped to FPGA and the hardware functionality is verified. The correct
classification rate of proposed system is 90.83%.
