A new feature extraction framework based on wavelets for breast cancer diagnosis
| Autor: | Semih Ergin, Onur Kilinc |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Support Vector Machine
Computer science business.industry Feature vector Feature extraction Scale-invariant feature transform Health Informatics Pattern recognition Breast Neoplasms Linear discriminant analysis Cross-validation Computer Science Applications Support vector machine Histogram of oriented gradients Pattern recognition (psychology) Image Interpretation Computer-Assisted Humans Female Artificial intelligence Diagnosis Computer-Assisted business Mammography |
| Zdroj: | Computers in biology and medicine. 51 |
| ISSN: | 1879-0534 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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