Of all the known cancers, breast cancer is the most widespread cancerous pathology among women. As causes of its onset are till unknown, there are no effective ways to prevent breast cancer. An efficient diagnosis in its early stage can thus give women a better chance of full recovery. Therefore, early detection of breast cancer is the key for reducing the associated morbidity and mortality rates. Mammography is the most effective and reliable method for breast cancer early detection. It is a radiological screening technique which makes the detection of breast lesions possible using low radiation doses. It allows breast cancer diagnosis at a very early stage when disease treatment by suitable therapies for the pathology regression/cure is still effective. In a widespread screening program, it is difficult for radiologists to provide both accurate and uniform evaluation particularly because of the large number of mammograms to be analyzed. Therefore, computer-aided detection (CAD) systems, which automatically detect signs of illness in its early stage, are important and necessary for breast cancer control. They provide a second opinion to help physicians detect abnormalities. Microcalcifications and masses are the two most important indicators of illness malignancy, and their automated detection is very valuable for early breast cancer diagnosis. The high correlation between the appearance of microcalcification clusters and diseases suggests that CAD systems for the automated detection of microcalcification clusters can be very useful and helpful for avoiding misdiagnosis and for early stage cancer detection. The present study summarizes the various methods adopted for microcalcification cluster detection and compares their performance. Moreover, reasons for the adoption of a common public image database as a test bench for CAD systems, motivations for further CAD tool improvements, and the effectiveness of various CAD systems in a clinical environment are given.