Integrating radiologist feedback with computer aided diagnostic systems for breast cancer risk prediction in ultrasonic images: An experimental investigation in machine learning paradigm

With advancements in machine learning algorithms and computer aided diagnostic (CAD) systems, the performance of automated analysis of radiological images has improved substantially in recent times. However, the lack of integration between the radiologist and CAD systems restrains the rate of progress as well as the reach of such advancements in clinical use. This article aims to improve the clinical efficiency of ultrasound based CAD systems for classification of breast lesions by integrating back-propagation artificial neural network (BPANN), support vector machine (SVM) and radiologist feedback. The acquired breast ultrasound images were subjected to wavelet based filtering in order to reduce speckle noise followed by feature extraction, feature selection and classification. Experiments on a database of 178 ultrasound images of breast anomalies (88 benign and 90 malignant) show that the proposed methodology achieves classification accuracy of 98.621% and 98.276%, respectively, when all 457 and 19 most relevant features selected by multi-criteria feature selection method were used for classification. The accuracy achieved is significantly higher than that using conventional classifiers based on BPANN and SVM. Further, it is found that integrating expert opinion in CAD systems improves its overall performance. The quantitative results obtained are discussed in light of some recently reported studies.