Study and evaluation of feature vector optimization and classic methods in automatic breast cancer detection

Breast cancer is known to be among the most prevalent cause of mortality among women. Since early breast cancer diagnosis increases survival chances, the development of a system with a highly accurate output to detect suspicious masses in mammographic images is of great significance. Thus, many studies have focused on the development of methods with favorable performance and acceptable accuracy to detect cancerous masses, proposed various techniques to diagnose breast cancer, and compared their accuracies. Most previous studies have used composite selection and feature reduction techniques to detect breast cancer and accelerate its treatment; however, most have failed to reach the desired accuracy due to the selection of ineffective features and the lack of a proper analytical method for the features. The present study reviews the methods proposed to detect breast cancer so far and analyzes the process of feature vector optimization techniques as well as the normal/abnormal and benign/malignant mass classification.