AUTOMATIC CLASSIFICATION OF BREAST CANCER

Abstract

: Breast cancer ranks first among the most common types of cancer, globally, regionally. Artificial intelligence plays an important role in medical sector, especially in improving healthcare for patients, in which the early detection and diagnosis of disease increasing the probability of recovery. This paper with the help of machine learning technique proposes to present a non-invasive method for diagnosing and classify breast diseases based on mammograms and ultrasound images, to extract the statistical features of them (smoothness, perimeter, area, concavity, compactness, symmetry, size, diameter, concave and radius), to identify the breast tissue as malignant tumor, or a benign tumor and predicting in the future at the long term to prevent it. Learning algorithms are used mainly: support vector machine (SVM), multilayer perceptron (MLP), naïve Bayes (NB) and Decision tree (DT) algorithms to build model capable of classifying the breast tissue into malignant or a benign, based on several features reached up to 30 features. The Results showed that SVM achieved higher accuracy which is reached up to 95.89%, followed by MLP classifier with 93.61%, and the NB accuracy which is reached up to 90.62%.