Abstract:
Breast cancer is the second most common
form of cancer among females and also the fifth
most cause of cancer deaths worldwide. The early
detection is the best form of cure and hence timely
and accurate diagnosis of the tumor is extremely
vital. The use of learning machine and artificial
intelligence techniques has revolutionized the
process of diagnosis of the breast cancer. In this
system Radial Basic Function Neural Network
with Gaussian Function in hidden layer is used to
classify the Breast Cancer. There are 327 records
to implement the system. In each record includes
12 attributes. This system consists of three phases:
preprocessing phase, training phase, testing
phase.In preprocessing step, convert the input
data into the binary number. In training phase, the
RBF neural network is used to train the input
vectors, symptoms of breast cancer. Twelve
attributes of training datasets are presented into
the input layer of the neural network. The RBF
neural network is trained with training data and
save the optimal parameters. In testing phase, the
testing data inputsinto the trained neural network
with optimal parameters.The system displays one
of five classes of breast cancer stages.
