A review of Deep learning in breast cancer

Since breast cancer is the second most common cause of death for women, timely, precise identification can lower the mortality rate. With the use of computers, radiologists may quickly find problems. Information on the detection and diagnosis of numerous diseases and anomalies may be found in medical photographs. Radiologists may examine the interior structure using a variety of modalities, and many forms of research have shown a lot of interest in these modalities. Each of these modalities is extremely important in various medical sectors. This article seeks to give a review that highlights recent advances in the detection and classification of breast cancer using machine learning and deep learning technology. It initially offers a summary of the various machine learning methodologies, followed by a summary of the different deep learning techniques and particular architectures for detecting and classifying breast cancer. To offer a thorough introduction to the field. This study concludes by summarizing the upcoming developments and difficulties in identifying and categorizing breast cancer.

Keywords:

_ Deep learning , Breast Cancer , Diverse Diseases