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Recognizing CT Imaging Signs of Lung Diseases Through a New Feature Selection Method- Fisher Criterion and Genetic Optimization

Recognizing CT Imaging Signs of Lung Diseases Through a New Feature Selection Method- Fisher Criterion and Genetic Optimization

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 Recognizing Common CT Imaging Signs of Lung Diseases Through a New Feature Selection Method Based on Fisher Criterion and Genetic Optimization

 Common CT imaging signs of lung diseases (CISLs) are de?ned as the imaging signs that frequently appear in lung CT images from patients and play important roles in the diagnosis of lung diseases. This paper proposes a new feature selection method based on FIsher criterion and genetic optimization, called FIG for short, to tackle the CISL recognition problem. In our FIG feature selection method, the Fisher criterion is applied to evaluate feature subsets, based on which a genetic optimization algorithm is developed to ?nd out an optimal feature subset from the candidate features. We use the FIG method to select the features for the CISL recognition from various types of features, including bagof-visual-words based on the histogram of oriented gradients, the wavelet transform-based features, the local binary pattern, and the CT value histogram. Then, the selected features cooperate with each of ?ve commonly used classi?ers including support vector machine (SVM), Bagging (Bag), Naive Bayes (NB), k-nearest neighbor (k-NN), and Ada Boost (Ada) to classify the regions of interests (ROIs) in lung C Timages into the CISL categories. In order to evaluate the proposed feature selection method and CISL recognition approach, we conducted the ?vefold cross-validation experiments on a set of 511 ROIs captured from real lung CT images. For all the considered classi?ers, our FIG method brought the better recognition performance than not only the full set of original features but also any single type of features. We further compared our FIG method with the feature selection method based on classi?cation accuracy rate and genetic optimization (ARG). The advantages on computation effectiveness and ef?ciency of FIG over ARG are shown through experiments.


 


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  • Model: PROJ5001
  • 999 Units in Stock
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This product was added to our catalog on Saturday 03 September, 2016.

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