Optimal Feature Selection and Classification of Liver Tumors in Computed Tomography Images
碩士 === 元智大學 === 資訊管理學系 === 98 === In this study, we propose to a computer-aided diagnosis (CAD) system for the classification of liver tumors in non-enhanced computed tomography (CT) images. This study aimed to classify liver tumors to assist doctors for further diagnosis. The proposed system con...
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ndltd-TW-098YZU053960232015-10-13T18:20:42Z http://ndltd.ncl.edu.tw/handle/38218997976940991718 Optimal Feature Selection and Classification of Liver Tumors in Computed Tomography Images 電腦斷層肝臟腫瘤影像之最佳化特徵選取與分類 Ya-Han Chang 張雅涵 碩士 元智大學 資訊管理學系 98 In this study, we propose to a computer-aided diagnosis (CAD) system for the classification of liver tumors in non-enhanced computed tomography (CT) images. This study aimed to classify liver tumors to assist doctors for further diagnosis. The proposed system consists of three parts. First, the feature extraction module extracts 102 statistical and texture features. Second, the feature selection module acquired the combination of the best features by integrating the particle swarm optimization (PSO) with support vector machine (SVM) to reduce the complexity of computation. Finally, a support vector machine based classification model was constructed to identify benign and malignant liver tumors. Experimental results show that the accuracy of the proposed CAD system for classifying liver tumors was 84.53%, the sensitivity was 80%, the specificity was 88.09%, the positive predictive value (PPV) was 88.77% and negative predictive value (NPV) was 84.01%.The accurate rate is up to 80 % both on benign and malignant tumors of the CT images. The proposed method can achieve the purpose of enhancing the accuracy of automatic identify effectively to assist further diagnosis. 郭文嘉 2010 學位論文 ; thesis 64 zh-TW |
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碩士 === 元智大學 === 資訊管理學系 === 98 === In this study, we propose to a computer-aided diagnosis (CAD) system for the classification of liver tumors in non-enhanced computed tomography (CT) images. This study aimed to classify liver tumors to assist doctors for further diagnosis. The proposed system consists of three parts. First, the feature extraction module extracts 102 statistical and texture features. Second, the feature selection module acquired the combination of the best features by integrating the particle swarm optimization (PSO) with support vector machine (SVM) to reduce the complexity of computation. Finally, a support vector machine based classification model was constructed to identify benign and malignant liver tumors. Experimental results show that the accuracy of the proposed CAD system for classifying liver tumors was 84.53%, the sensitivity was 80%, the specificity was 88.09%, the positive predictive value (PPV) was 88.77% and negative predictive value (NPV) was 84.01%.The accurate rate is up to 80 % both on benign and malignant tumors of the CT images. The proposed method can achieve the purpose of enhancing the accuracy of automatic identify effectively to assist further diagnosis.
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author2 |
郭文嘉 |
author_facet |
郭文嘉 Ya-Han Chang 張雅涵 |
author |
Ya-Han Chang 張雅涵 |
spellingShingle |
Ya-Han Chang 張雅涵 Optimal Feature Selection and Classification of Liver Tumors in Computed Tomography Images |
author_sort |
Ya-Han Chang |
title |
Optimal Feature Selection and Classification of Liver Tumors in Computed Tomography Images |
title_short |
Optimal Feature Selection and Classification of Liver Tumors in Computed Tomography Images |
title_full |
Optimal Feature Selection and Classification of Liver Tumors in Computed Tomography Images |
title_fullStr |
Optimal Feature Selection and Classification of Liver Tumors in Computed Tomography Images |
title_full_unstemmed |
Optimal Feature Selection and Classification of Liver Tumors in Computed Tomography Images |
title_sort |
optimal feature selection and classification of liver tumors in computed tomography images |
publishDate |
2010 |
url |
http://ndltd.ncl.edu.tw/handle/38218997976940991718 |
work_keys_str_mv |
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1718030201920684032 |