Current Trends and Future Prospects: Detection of Breast Cancer Using Machine Learning Techniques

The root cause of death among the global population is considered Cancer. Cancer is originated from the compulsive accumulation of cells forming a tumor. One among these cancers is breast cancer. The 2020 cancer report states that 19.3 breast cancer cases were estimated and 10 million cancer deaths. In the past few years, a significant increase in breast cancer among women was perceived and it stands at fifth position among all the cancers, where 11.7% of female breast cancer cases were recorded according to the 2020 cancer report and 6.9% of deaths are caused. Many of the researchers noticed that the implementation of various machine learning techniques like feature extraction, feature selection, and classification can ease the task of physicians in breast cancer detection and diagnosis. Timely detection of breast cancer can save many lives and improve the treatment process. Thermograph is the most suitable screening technique for all age groups and is affordable compared to mammogram, Magnetic Resonance Imaging (MRI), and ultrasound. Precise tumor classification alleviates patients from pain and assists physicians with an accurate diagnosis. According to the survey done the main aim of many researchers is to implement automated and apparent breast cancer identification and classification system. Our paper presents the review of the most updated machine learning techniques and methods implemented by various researchers’ for detecting, and diagnosing breast cancer. And future directions are outlined regarding various machine learning algorithms like SVM, ANN, and K-NN utilized for detecting breast cancer.