Using Multilayer Perceptron and Deep Neural Networks for the Diagnosis of Breast Cancer Classification

Abstract

The very long delay that is suffered by patients of breast cancer in
their early stages in low-income countries is due to access barriers
and quality deficiencies in the care of cancer giving rise to the need
for an alternative and efficient computer-based diagnostic system for
the early detection and prevention of the disease. The early detection
and improved therapy still remain a crucial approach for the
prevention and cure of breast cancer. To this end, recent research
looks into the development of different classifier models for the
classification of breast cancer. This paper investigates the potentials
of applying multiple neural network architectures with increased
number of hidden layers and hidden units. The network architectures
have one-hidden-layer, two-hidden-layer and three hidden layer
(deep neural network) architectures respectively using the
backpropagation training algorithm for the training of the models.
The experimental results show that by applying this approach the
models yield efficient and promising results