Gray level co-occurrence matrix feature extraction and histogram in breast cancer classification with ultrasonographic imagery

Abstract

One way to detect breast cancer is using the ultrasonography (USG) procedure, but the ultrasound image is susceptible to the noise speckles so that the interpretation and diagnosis results are different. This paper discusses the classification of breast cancer ultrasound images that aims to improve the accuracy of the identification of the type and level of cancer malignancies based on the features of its texture. The feature extraction process uses a histogram which then the results are calculated using the gray level co-occurrence matrix (GLCM). The results of the two extraction features are then classified using k-nearest neighbors (KNN) to obtain accurate figures from those images. The results of this study is that the accuracy in detecting cancer types is 80%.