An Improved Approach for Computer-Aided Diagnosis of Breast Cancer in Digital Mammography

Breast cancer keeps on being a major medical problem among women around the world. Early detection of breast cancer can expand the treatment options and consequently would increase the surviving possibilities for patients. In this paper, a new Computer Aided Diagnosis (CAD) system is proposed for breast cancer diagnosis in digital mammography. An improved technique for feature extraction based on Wavelet-Based Contourlet Transform (WBCT) is investigated to obtain the features of the Region of Interest (ROI), allowing for accuracy improvement over other standard approaches. Aiming to reduce the features dimensions, we have proposed a hybrid feature selection approach in which the Genetic Algorithm (GA) and the Support Vector Machine (SVM) are combined along with the Mutual Information (MI) in order to select the best combination of tumor indicators, with maximal discriminative ability. The Particle Swarm Optimization (PSO) is also investigated instead of GA for performance evaluation of both methods. The selected features are then submitted to the kernel SVM classifier and its performance is compared with the traditional machine learning classification techniques. The diagnosis accuracy of the implemented CAD system is demonstrated by investigating several experimental datasets and comparing the results with other diagnosis approaches. The results show that the proposed CAD system (WBCT + GA-SVM-MI + kernel SVM) is superior over other techniques in terms of the classification accuracy (97.5% for normal-abnormal and 96% for benign-malignant), while keeping the computational requirements as low as possible.