Cancer is one of the most dangerous diseases around the world and
the most common cancer among women is breast cancer. Although not all the cancer types are curable upon diagnosis, breast cancer can be cured if it is diagnosed early. The most reliable way of diagnosing reast cancer is mammographic screening which can diagnose the disease 1.5 to 4 years before it is clinically diagnosed. Double Reading is the important diagnostic process in which two experts/radiologists should read the same mammogram image to make an accurate diagnosis. But this process is not a cost-effective approach for early detection of breast cancer. Computer-Aided Diagnosis (CAD) can act as the second expert and therefore one expert would be enough for breast cancer diagnosis. In this study, we use the data extracted from low-resolution as well as high resolution mammography images. The attributes extracted from mammographic images are imported into Support Vector Machine (SVM) to classify the patients. An important point about the attributes is that sometimes there may be some irrelevant or even noisy attributes that have negative effect on the classification accuracy. Therefore, the main objective of this study is to apply local and global search paradigms in order to find the best subset of attributes to construct the most accurate CAD system that can effectively distinguish between benign and healthy patients. Artificial Bee Colony (ABC) is a population-based swarm intelligence algorithm with good global exploration ability, and Simulated Annealing (SA) is a robust local-search algorithm. Thus, we utilize a hybrid global and local search algorithm (named ABCSA) to simultaneously benefit from the advantages of both ABC and SA. In this approach, ABC is firstly performed for the global exploration in the search space. Then, SA is utilized to search locally in the vicinity of the best solution found via ABC, in order to improve the quality of the final solution. Obtained simulation results over four different mammographic datasets show that the proposed algorithm outperforms the existing metaheuristic feature selection approaches in terms of minimizing the number of features, while maximizing the detection accuracy
