Abstract: A system for automated detection of cancerous masses in mammograms initially identifies regions of interest (ROIs) using Fourier analysis (e.g., by means of an optical correlator). Context data is extracted from the mammogram for each ROI, such as size, location, ranking, brightness, density, and relative isolation from other ROIs. The pixels in the ROI are averaged together to create a smaller array of super-pixels, which are input into a first neural net. A second neural net receives the output values from the first neural net and the context data as inputs and generates an output score indicating whether the ROI contains a cancerous mass. The second neural net can also be provided with context data from another view of the same breast, the same view of the other breast, or a previous mammogram for the same patient.