Breast cancer diagnosis based on support vector machine techniques

Abstract

In general, breast cancer is a fatal disease; however, early detection can significantly reduce the risk of death. A physician's experience in detecting and diagnosing breast саnсеr can be aided by automated feature extraction аnd classification procedures. Clinical exams and imaging studies are typically used to make a diagnosis of breast cancer. Mammography is by far the most common imaging technique used to detect the early warning signs of breast cancer. The goal of this paper is to design a computer-aided diagnosis/detection (CAD) system by utilizing image processing techniques. These techniques will represent the first stage in the system, and they will significantly contribute to improving diagnostic accuracy. Next is the “Histogram of oriented gradients (HOG)” technique, which was used to extract features. The final stage involves applying machine learning techniques (MLT), in this case the support vector machine (SVM), a widely used method for detecting breast cancer using mammograms. In testing, the proposed model was found to be 94.74% accurate.