Early prognosis of breast cancer using image processing and machine learning

Abstract

Among females, leading cause of cancer death and the most common cancer type is breast cancer. Early detection is vital because it reduces the mortality rate. Digital mammography is a widespread medical imaging technique that is used for early detection and diagnosis of the breast cancer. Automatic detection of tumorous area from the digital mammography image helps to locate the abnormal tissues, which may be analyzed further by a radiologist. It has two main stages: feature extraction and classification. In this work, numerous feature extraction methods have been tested such as 2D-DWT, HOG, Haralick’s textural features, TAS, LBP, Zernike and GLCM. In order to select the most suitable classifier, the following classifiers also have been tested: random forest, logistic regression, k-nearest neighbors, naïve Bayes, decision tree, support vector machines, Adaboost, radial basis function network, multilayer perceptron, convolutional neural network. Based on comprehensive experiments, the optimum combination of feature extraction, feature selection and classification methods are identified. The proposed method, which employs CLAHE as image pre-processing tool, 2D-DWT, HOG, Haralick as feature extraction methods, wrapper as the feature selection method and random forest as the classifier, attained an accuracy of 87.5%