MAMMOGRAM CLASSIFICATION USING MACHINE LEARNING

The most prevalent type of cancer in women, breast malignancy affects people all over the world. The death rate associated with breast cancer is exceptionally high in comparison to that of other illnesses. The medical and biomedical engineering communities’ primary focus is on improving breast cancer patients’ chances of surviving the disease. Early detection is beneficial for patients who have breast malignancy. Biomedical engineers have developed a variety of screening and computer-aided diagnostic (CAD) approaches in order to detect breast malignancy at an earlier stage. Mammograms are absolutely necessary for the early detection of breast malignancy. Mammography is a critical medical imaging tool for detecting and diagnosing breast malignancy in its earliest stages. Mammography’s primary purpose is to perform tests and examinations on the breast in order to identify abnormal growths of tumours. The following is an outline of the mammography process: A radiologist will go at the data either after they have been recorded on X-ray film or instantly entered into the computer. Mammography is a tool that a gynaecologist can use to detect anomalies in the breast tissue of a patient that cannot be seen by the naked eye during a physical examination. The diagnostic procedure may be sped up with the help of this method, which in turn increases the proportion of criteria that can be relaxed. Over the past few years, a significant amount of progress has been made in this area. Because breast malignancy is so common and has such a wide reach, there is a pressing need to develop better diagnostic techniques for the illness. As a consequence of this, there is a demand in the field of medical image processing for efficient tools and methods that may be employed in the detecting process, identification, and categorization of breast malignancy tissue.