Breast tumor detection in digital mammography based on extreme learning machine

Breast tumor detection in digital mammography is one of the most important methods of breast cancer prevention. Computer-aided diagnosis (CAD) based on extreme learning machine (ELM) has significant meanings for breast tumor detection as it has good generalization abilities and a high learning efficiency. In this paper, a breast tumor detection algorithm in digital mammography based on ELM is proposed. First, a median filter is used for noise reduction, and contrast enhancement of the digital mammography in data preprocessing. Next, methods of wavelet modulus maxima transform, morphological operation and region growth are used for the breast tumor edge segmentation. Then, five textural features and five morphological features are extracted. Finally, an ELM classifier is used to detect the breast tumor. Comparing breast tumor detection based on Support Vector Machines (SVM), with breast tumor detection based on ELM, not only does ELM have a better classification accuracy than SVM, but it also has a greatly improved training speed.