A survey: Breast Cancer Classification by Using Machine Learning Techniques

Abstract

Breast cancer in general is a common and deadly disease. Early detection can significantly reduce the chances of death. Using automated feature extraction and classification algorithms, physicians' experience in diagnosing and detecting breast cancer can be aided. This paper focuses on various statistical and machine learning studies of mammography datasets for enhancing the accuracy of breast cancer diagnosis and classification based on various variables. The Naïve Bayes, the K-nearest neighbors (KNN), the Support Vector Machine (SVM), the Random Forest, the Logistic Regression, Multilayer Perceptron (MLP), fuzzy classifier, and Convolutional Neural Network (CNN) classifiers, are the most widely used technologies in this field. In this study, we provide an overview of the existing CAD systems based on artificial intelligence classification techniques and many types of medical image modalities. Potential research initiatives to build more efficient and accurate CAD systems have been investigated.