Multi-Variate and Multi-dimensional CFAR Detection of Breast Cancer

Abstract Breast cancer is the most common type of cancer in females. In many cases, the mortality rate can be drastically lowered if the disease is detected early. Due to its safety and lack of risk to the patient, microwave breast imaging is considered a potential replacement for mammography. This paper presents a breast cancer detection approach based on the Multi-Variate and Multi-Dimensional Constant False Alarm Rate (MVMD-CFAR) method. This method has several advantages over mammography using x-rays, including increased patient comfort and lower costs. On an open-source experimental database derived from the University of Manitoba Microwave Mammography Dataset UM-BMID, the performance of the (2D-CFAR) method is evaluated by examining the available data set for breast microwave sensing. We segregate infected and healthy samples and assessed the probability density function PDF for pictures of normal and malignant tissue. The third dimension of the algorithm is the image's color data, which comprises three variables (three colors). Initial testing show that the MVMD-CFAR detector is highly effective, with a detection probability of 97.4% and a false alarm probability of 10%. However, a few challenges must be overcome before this imaging technique can reach its full potential and be implemented in clinical settings.