Feature selection algorithm based on binary BAT algorithm and optimum path forest classifier for breast cancer detection using both echographic and elastographic mode ultrasound images.

Abstract

Breast cancer is one of the fatal diseases among women. Every year, its incidence and mortality rate increase globally. Mammography and sonography are widely used in breast cancer detection. Because mammography misses many cancers and shows false negatives in the denser tissues, sonography is preferred to give some extra information in addition to that available from mammography. To improve the performance of breast cancer detection by reducing false positives. The local binary pattern (LBP) texture features must be extracted from ultrasound elastographic and echographic images of the same patients and then fused to form a single feature vector. Local Binary Pattern (LBP) texture features of elastographic and echographic images are extracted, and reduced individually through a hybrid feature selection technique based on binary BAT algorithm (BBA) and optimum path forest (OPF) classifier and then fused serially. Finally, the support vector machine classifier is used to classify the final fused feature set. Various relevant performance metrics such as accuracy, sensitivity, specificity, discriminant power, Mathews correlation coefficient (MCC), F1 score, and Kappa were used to analyze the classification results. The use of LBP feature produces 93.2% accuracy, 94.4% sensitivity, 92.3% specificity, 89.5% precision value, 91.88% F1 score, 93.34% balanced classification rate, and Mathews correlation coefficient of 0.861. The performance was compared with gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM), and LAWs features, and showed that LBP outperformed. Because the specificity is better, this method could be useful for detecting breast cancer with minimum false negatives.