کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6921629 | 864505 | 2014 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A new feature extraction framework based on wavelets for breast cancer diagnosis
ترجمه فارسی عنوان
یک چارچوب استخراج ویژگی جدید بر اساس موجک برای تشخیص سرطان پستان
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کلمات کلیدی
سرطان پستان، استخراج ویژگی، ماموگرافی دیجیتال، تشخیص کامپیوتری،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
This paper investigates a pattern recognition framework in order to determine and classify breast cancer cases. Initially, a two-class separation study classifying normal and abnormal (cancerous) breast tissues is achieved. The Histogram of Oriented Gradients (HOG), Dense Scale Invariant Feature Transform (DSIFT), and Local Configuration Pattern (LCP) methods are used to extract the rotation- and scale-invariant features for all tissue patches. A classification is made utilizing Support Vector Machine (SVM), k-Nearest Neighborhood (k-NN), Decision Tree, and Fisher Linear Discriminant Analysis (FLDA) via 10-fold cross validation. Then, a three-class study (normal, benign, and malignant cancerous cases) is carried out using similar procedures in a two-class case; however, the attained classification accuracies are not sufficiently satisfied. Therefore, a new feature extraction framework is proposed. The feature vectors are again extracted with this new framework, and more satisfactory results are obtained. Our new framework achieved a remarkable increase in recognition performance for the three-class study.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Biology and Medicine - Volume 51, 1 August 2014, Pages 171-182
Journal: Computers in Biology and Medicine - Volume 51, 1 August 2014, Pages 171-182
نویسندگان
Semih Ergin, Onur Kilinc,