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Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight women. Currently there are three techniques to diagnose breast cancer: mammography, FNA (Fine Needle Aspirate) and surgical biopsy. In this paper, we develop a system that can classify “Breast Cancer Disease” tumor using neural network with Feed-forward Backpropagation Algorithm to classify...
This paper compared various MLP activation functions for classification problems. The most well-known (Artificial Neural Network) ANN architecture is the Multilayer Perceptron (MLP) network which is widely used for solving problems related to data classifications. Selection of the activation functions in the MLP network plays an essential role on the network performance. A lot of studies have been...
Diagnosis of diseases is well known problem in the medical field. Past research shows that medical database of disease can be train by using various neural network models. Many medical problems face the problem of curse of dimensionality due to the excessively large number of input attributes. Breast cancer is one such problem. We propose the use of modular neural network for effective diagnosis....
This paper presents a novel approach to simulate a Knowledge Based System for diagnosis of Breast Cancer using Soft Computing tools like artificial neural networks (ANNs) and Neuro Fuzzy Systems. The feed-forward neural network has been trained using three ANN algorithms, the back propagation algorithm (BPA), the radial basis function (RBF) Networks and the learning vector quantization (LVQ) Networks;...
A major class of problems in medical science involves the diagnosis of a disease based upon various tests performed upon the patient. Cancer is a complex and clinical heterogeneous disease. The research into the diagnosis and treatment of cancer has become an important issue for the scientific community. The objective of cancer classification is to design a classifier to categorize the tissue samples...
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