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Study of Computer Aided Diagnosis for the Improvement of Survival Rate of Lung Cancer based on Adaboost Learning  

Won, Chulho (경일대학교 의용공학과)
Publication Information
Journal of rehabilitation welfare engineering & assistive technology / v.10, no.1, 2016 , pp. 87-92 More about this Journal
Abstract
In this paper, we improved classification performance of benign and malignant lung nodules by including the parenchyma features. For small pulmonary nodules (4-10mm) nodules, there are a limited number of CT data voxels within the solid tumor, making them difficult to process through traditional CAD(computer aided diagnosis) tools. Increasing feature extraction to include the surrounding parenchyma will increase the CT voxel set for analysis in these very small pulmonary nodule cases and likely improve diagnostic performance while keeping the CAD tool flexible to scanner model and parameters. In AdaBoost learning using naive Bayes and SVM weak classifier, a number of significant features were selected from 304 features. The results from the COPDGene test yielded an accuracy, sensitivity and specificity of 100%. Therefore proposed method can be used for the computer aided diagnosis effectively.
Keywords
Lung Cancer; Lung Nodule; Adaboost; Computer Aided Diagnosis; CT data;
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