Detection of clustered microcalcifications using fuzzy modeling and convolution neural network

This paper describes an automatic computer searching system for detecting clustered microcalcifications. A fuzzy classification modeling was employed to extract each suspected microcalcification possessing similar physical parameters. Therefore, only those possible classes were evaluated using a sophisticated convolution neural network which requires a great deal of computation and serves as a discriminator. Based on the detected spots, many of them are true microcalcifications, the computer can easily make a determination when 5 spots are located within a defined region. However, when a cluster consists of only two to four suspicious spots a fuzzy function was used to determine the inclusion of other spots near the cluster. This can be very important for the detection of subtle cases. The membership of the latter fuzzy function was composed of the distance between the suspected spots as well as the output values of the convolution neural network. We have tested the improved algorithms on our research database consisting of 45 mammograms. The results indicated that the fuzzy classification modeling decreased the number of false-positives from 2,874 to 1,067 suspected spots per image without increasing any false-negative detection. The over-all performance in the detection of clustered microcalcifications through the updated algorithms was 90% sensitivity at 0.5 false-positive per image. The computation time using a DEC-Alpha workstation was decreased from 5 minutes to about 3 minutes per image.