A fully-automated computer-aided breast lesion detection and classification system

•A comprehensive and fully automatic decision support system is designed for breast lesion detection and classification.•A two-stage segmentation procedure is proposed.•Histogram, shape, GLCM, GLRLM, NGTDM, and GLDM based 88 features are derived to characterize the breast lesions.•SVM, KNN, RF and NB techniques are performed to classify breast lesions as benign or malignant. This study presents an automatic computer-aided detection and diagnosis system which consists of two parts. The first part is for breast lesion characterization developed in pattern recognition framework (K-means clustering method) which is important to provide useful information for breast lesion characterization. Characterization of the detected lesion areas is done based on 6 parameters that are: (1) histogram, (2) shape, (3) gray level co-occurrence matrix, (4) gray level run length matrix, (5) neighboring gray tone difference matrix, and (6) gray level dependence matrix features. The second part of the system is developed based on machine learning algorithms and serves for the classification of localized breast lesions as benign and malignant. For classification, 4 different machine learning algorithms were investigated: (1) support vector, (2) k-nearest neighbors, (3) random forest, and (4) naïve Bayes classifiers. 84 histopathologically proven breast lesions were analyzed in the study. The proposed system compensates the motion artifacts, segments breast lesions, and classifies the lesions as benign and malignant. The results prove that the developed comprehensive system can detect and classifies breast lesions without any intervention. The best accuracy, sensitivity, specificity, and precision values to decide the tumor aggressiveness are 90.36%, 96.25%, 83.33%, and 92%, respectively.