A Systematic Literature Review on the Methods of Breast Cancer Classification

Abstract

Cancer is the second most common cause of death in the world. WHO notes, deaths caused by cancer will reach 10 million cases in 2021. Of many cancers, breast cancer is a cancer with the most cases. Early diagnosis of breast cancer plays an important role in the treatment process. Various imaging methods, including magnetic mammography, are used to diagnose breast cancer. With the help of machine learning, the process of diagnosing breast cancer with mammography images is more precise and accurate. Various machine-learning methods have been developed by researchers to diagnose breast cancer. Among them is a deep learning method that can achieve good feature representation and can solve the problem of image classification and object localization. Through a systematic literature review, this research collects and analyzes related studies regarding the classification of breast cancer that have been done previously. Several aspects that will be evaluated include the methods used, data sources used, and accuracy of the method used. This research is expected to provide clear knowledge about the advantages and disadvantages of using artificial intelligence techniques for breast cancer classification. The results of this study can provide insight for researchers and medical practitioners in the further development and application of deep learning methods in the diagnosis and classification of breast cancer.