Feature extraction method for breast cancer diagnosis in digital mammograms using multi-resolution transformations and SVM-fuzzy logic classifier

摘要

The paper comprises a pattern to study digital mammogram images, which is mainly oriented to the recognition of the malignant/benign masses, skin thickening and micro calcifications. Image processing algorithms, like multi-resolution transformations, are implemented to obtain vector coefficients; wherein, a matrix is obtained on the basis of wavelet coefficients. Texture features are extracted from the wavelet coefficients. Dynamic thresholds are applied to optimise the number of features, and achieve maximum classification accuracy rate. The SVM-fuzzy method is used to classify between normal and abnormal tissues. The fuzzy classifier is used for extracting geometrical features. Due to lack of generalisations, the neuro-fuzzy rule is integrated with a kernel SVM to form a support vector based on the neural network in handling the uncertainty information. We use the Mammographic Image Analysis Society (MIAS) standard data set for the study and training-set purpose. We obtain classification accuracy rates of about 93.9%, demonstrating the proposed method for contribution towards a successful diagnosis pattern for breast cancer.