Mammogram Image Classification Using Various Machine Learning Algorithms

Abstract

The leading cause of death in women is still breast cancer.Detecting cancer in its early stages is crucial. For the purpose of diagnosing breast cancer data, a variety of machine learning algorithms are available.In this study, performance comparisons between different machine learning algorithms: Extra Trees, Random Forest, Support Vector Machine (SVM),Decision Tree, Logistic Regression Bagging, Gradient Boosting, and AdaBoost have been conducted on mammography images of MIAS(Mammographic Image Analysis Society) database.It is observed that Bagging outperformed all other algorithms and achieved the highest accuracy (0.9678).All the work is done in the Kaggle environment based on the python programming language.