Computer-aided Diagnosis Using Neural Networks and Support Vector Machines for Breast Ultrasonography

Modern medical ultrasound equipment performs real-time high-resolution imaging without the use of ionizing radiation. The cost-effectiveness and portability of this facility are particularly important in small-scale hospitals, in which the equipment is useful in conducting complex medical imaging in a timely manner. The use of ultrasonic images to analyze the homogeneity of an internal echo is important to physicians in making diagnostic decisions. However, medical ultrasound images contain significant speckles, noises and ultrasound examination is operator dependent owing to experiences of the interpreter. A computer-aided diagnosis (CAD) system will provide a second beneficial opinion and avoid inter-observer variation. Hence CAD has become a major research topic in medical ultrasound imaging and diagnosis. The artificial neural networks and support vector machines (SVMs) models are extensively used in classification for its ability to model the complex system. Various breast ultrasound CAD systems using the neural network and SVM algorithms have been proposed and the results demonstrated that the classification models have theirs potential effectiveness. This article will review the applications of neural network and SVM in the current breast CAD systems of ultrasound.