iz

Cancer is a rouge disease causing billion of human lives every year. The disease occurs when the cells start dividing uncontrollably and spreading into different body tissues. Among carcinoma, breast tumor is the most common cancer in females and the vital reason for female death. Although the disease is curable and can be controlled, it requires an early diagnosis and prognosis for the patient survival. Artificial intelligence and machine learning have recently gained great success and have been successfully applied to solving real-life problems. Also, these technologies have been assisting the medical field in various areas, like disease classification and prediction, using multiple methods. For example, the models have been applied in radiology for breast cancer detection and prediction. In this paper, we will review the computer-aided diagnosis (CAD) models for breast cancer diagnosis using machine learning (ML) and deep learning (DL). Also, compare these methods, including their details, along with the pros and cons of each model. This review also highlights the open challenges, research issues, and future work to improve the breast cancer diagnosis system. ML and DL are the subfields of artificial intelligence (AI.) and are widely used in the healthcare industry for excellent results and cost-effective solutions. The primary purpose of this study is to identify the different machine learning and deep learning classifier for breast tumor diagnosis. We also compare the results of the classifiers for performance measures. The research gap will guide the researchers to make improvements for better results.

Keywords: Breast Cancer Classification, Machine Learning Classification, Deep Learning, Convolution Neural network, Computer-aided Diagnosis, Breast Cancer.