An Accurate Intelligent Breast Cancer Diagnosis System

Early detection of the breast cancer can significantly increase survival rate among women. Nowadays, researchers aim to automatize fine needle aspiration (FNA), as a simple, non-expensive and non-invasive test for breast cancer diagnosis. Intelligent diagnosis of breast cancer consists of 5 steps: fluid extraction from the breast lump, capturing digital microscopic images from the samples, extracting morphological real-valued features from the images, feature selection and designing a pattern recognition system to distinguish between benign and malignant tumors. Using WDBC database (including 569 FNA samples), a novel BPSO-based feature selection method and SVM classifiers an intelligent breast cancer diagnosis system is developed. Merit of the proposed system is successfully certified on WDBC dataset leading to recognition rate of %100 using only 28 features (in 5 SVM models). The system clearly outperforms previous works in both respects of accuracy and the number of required features. Developing a novel efficient feature selection algorithm can improve both accuracy and speed of intelligent breast cancer diagnosis systems. In addition to general diagnosis, using feature selection would help physicians discovering abnormalities caused by diseases.