期刊
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
卷 56, 期 8, 页码 1475-1485出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s11517-017-1774-z
关键词
Micro-calcification; Classification; Fisher discriminant analysis; Principal component analysis; Computer aided detection; Dimensionality reduction
Breast cancer is one of the major causes of death in women. Computer Aided Diagnosis (CAD) systems are being developed to assist radiologists in early diagnosis. Micro-calcifications can be an early symptom of breast cancer. Besides detection, classification of micro-calcification as benign or malignant is essential in a complete CAD system. We have developed a novel method for the classification of benign and malignant micro-calcification using an improved Fisher Linear Discriminant Analysis (LDA) approach for the linear transformation of segmented micro-calcification data in combination with a Support Vector Machine (SVM) variant to classify between the two classes. The results indicate an average accuracy equal to 96% which is comparable to state-of-the art methods in the literature.
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