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This paper investigates the use of neural networks to detect and locate early breast cancer using a simple feed-forward back-propagation neural network. In order to test the proposed algorithm, an electromagnetic simulator is used to build a three-dimensional breast model. Spherical tumors of radii 1 mm, 2 mm, 4 mm, and 5 mm are assumed to be at different locations in the breast model. An ultra-wideband...
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...
Clustered micro calcifications (MCs) are one of the early signs of breast cancer. In this paper, we propose a new computer aided diagnosis (CAD) system for automatic detection of MCs in two steps. First, pixels corresponding to potential micro calcifications are found using a multilayer feed-forward neural network. The input of this network consists of 4 wavelet and 2 gray-level features. The output...
This paper presents a Lempel Ziv Complexity (LZC) based pruning algorithm, called Silent Pruning Algorithm (SPA), for designing artificial neural networks (ANNs). This algorithm prunes hidden neurons during the training process of ANNs according to their ranks computed with LZC. LZC extracts the number of unique patterns in a time sequence as a measure of rank. As a result, it is expected that LZC...
During radiation treatment of lung cancer patients, synchronization of treatment devices and tumor position has been a challenging task due to breathing-induced tumor motion and treatment device latency. One method to mitigate the impact of system latency is to determine the future tumor position. Breathing prediction presents a methodology to determine future tumor position indirectly. Different...
This paper presents a novel technique for the supervised training of feed-forward artificial neural networks (ANN) using the Harmony Search (HS) algorithm. HS is a stochastic meta-heuristic that is inspired from the improvisation process of musicians. Unlike Backpropagation, HS is non-trajectory driven. By modifying an existing improved version of HS & adopting a suitable ANN data representation,...
This paper deals with the comparison of the two neural network methods of learning: supervised (classical feedforward neural networks: multi-layer neural networks (MLP), radial basis function (RBF) and probabilistic neural networks (PNN)) and unsupervised (self organizing feature maps (SOFM), or Kohonen map), in order to assess their performances on a labeled breast cancer database. By revealing their...
Liquid chromatography tandem mass spectrometry (LC/MS/MS) based plasma proteomics profiling technique is a promising technology platform to study candidate protein biomarkers for complex human diseases such as cancer. Factors such as inherent variability, protein detectability limitation, and peptide discovery biases among LC/MS/MS platforms have made the classification and prediction of proteomics...
In the last decade, the use of artificial neural networks (ANN) has become widely accepted in medical applications for accuracy for predictive inference, with potential to support and flexible non-linear modelling of large data sets. Feedforward neural network (FNN) is a kind of artificial neural networks, which has a better structure and been widely used. But there are still many drawbacks if we...
The identification of the state of human skin tissues is discussed here. The bio-optical signals recorded in vitro have been analyzed by extracting various statistical features. Using LAB VIEW 7.1 programs/tools, different statistical features are extracted from both normal and pathology spectra. Each spectrum is filttered and normalized. Then different features like skewness, summation, median residuals,...
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