A Survey on Machine Learning Techniques for Breast Cancer Diagnosis and Detection

Abstract

With the advancements in modern engineering techniques there is a considerable improvement in medical field diagnosis today. Utilization of modern machine learning techniques in the image processing domain for Cancer detection helps both doctors and patients for understanding the level of infection properly. This paper presents an overview of the different methodologies for the classification and detection of breast cancer with the help of deep learning and machine learning techniques. We present the review of various algorithms like Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN) and Naive Bayes (NB). The primary goal of this review is to show comparison, to summarize the challenges, and future trends in the detection and classification of breast cancer. Various publications and databases were accessed as a source of information to conduct this review.