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This paper presents a computer-aided diagnosis (CAD) system based on combined support vector machine (SVM) and linear discriminant analysis (LDA) classifier for detection and classification breast cancer in digital mammograms. The proposed system has been implemented in four stages: (a) Region of interest (ROI) selection of 32??32 pixels size which identifies suspicion regions, (b) Feature extraction...
In this paper, we introduce a method of functionally classifying lung cancer cells from normal cells by using Tetrakis Carboxy Phenyl Porphine (TCPP) and well-known computational intelligent techniques. Tetrakis Carboxy Phenyl Porphine (TCPP) is a porphyrin that is able to label cancer cells due to the increased numbers of low density lipoproteins coating the surface of cancer cells and the porous...
Feature selection plays an important role in cancer classification, for gene expression data usually have a large number of dimensions and relatively a small number of samples. In this paper, we use the support vector machine (SVM) for cancer classification. We propose a mixed two-step feature selection method. The first step uses a modified t-test method to select discriminatory features. The second...
Summary form only given. The current molecular biology and systems biology is featured by the rapid accumulation of high-throughput genomics and proteomics data like microarray and mass spectrometry (MS) data. Through our study on microarray and MS data, we have observed that the cancer classification and gene/biomarker selection task has many unique characteristics that distinguish itself from other...
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