کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4973371 | 1451640 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Lung nodule classification using local kernel regression models with out-of-sample extension
ترجمه فارسی عنوان
طبقه بندی نودولهای ریوی با استفاده از مدلهای رگرسیون محلی کرنل با فرمت خارج از نمونه
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کلمات کلیدی
خوشه طیفی، رگرسیون خطی، ترفند هسته، ندولهای ریه، خارج از نمونه،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
Computer-aided classification is a major research task for computer-aided diagnosis of pulmonary nodules. In radiology domain, labeled data can be expensive to generate. Therefore, in this study, a novel unsupervised spectral clustering algorithm was presented to distinguish benign and malignant nodules. In this algorithm, a new Laplacian matrix was constructed by using local kernel regression models (LKRM) and incorporating a regularization term, the regularization term can tackle the out-of-sample problem. To verify the feasibility of our algorithm, a ground truth dataset was assembled from the LIDC-IDRI database, including 371 benign and 375 malignant lung nodules. All nodules were represented by the texture features, which were computed from the regions of interest (ROIs). Extensive experiments on lung nodules showed that the proposed algorithm not only achieved a higher classification performance than existing popular unsupervised algorithms, but also had superiority comparing to some supervised algorithms (linear discriminant analysis and extreme learning machine).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biomedical Signal Processing and Control - Volume 40, February 2018, Pages 1-9
Journal: Biomedical Signal Processing and Control - Volume 40, February 2018, Pages 1-9
نویسندگان
Guohui Wei, He Ma, Wei Qian, Fangfang Han, Hongyang Jiang, Shouliang Qi, Min Qiu,