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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...
Mining techniques are needed to extract important information from huge high dimensional gene expression sets. Targeting unique expression behavior as over/under-expression is specific to gene expression data and is needed to explore another direction in the relation of genes to tumor conditions. This research proposes criteria for filtering over-expression genes, identifying over-expression related...
Breast cancer is the second most common cancer worldwide and the fifth most common cause of cancer death. There are many prognostic factors associated with breast cancer which are usually considered when determining how cancer will affect a patient. In addition, distinct molecular subtypes of breast tumors have been described by gene expression profiling. In this work we integrate information from...
Cancer research has become an extremely data rich environment, with huge batteries of tests performed to quantify and categorise tumours. Multiple analyses of biochemical, molecular and immunohistological markers on tissue samples generate large and complex data sets to compare with clinical and pathological parameters. The manual data analysis procedures by scientists have become impractical and...
In this paper we propose an efficient algorithm based on principal component analysis (PCA) and fuzzy support vector machine (SVM) for the diagnosis of breast cancer tumor. First, PCA algorithm is implemented to project high-dimensional breast tumor data into much lower dimensional space, then the processed data are classified by a fuzzy SVM classifier. Experimental and analytical results show that...
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