Use of a constraint satisfaction neural network for breast cancer diagnosis and dynamic scenarios simulation
A constraint satisfaction neural network (CSNN) has been developed for breast cancer diagnosis from mammographic and clinical findings. CSNN is a circuit network aiming to maximize the activation of its nodes given the constraints existing among them. The constraints are built into the network weights. An autoassociative backpropagation (auto-BP) learning scheme is initially used to determine the CSNN weights. During the training phase, the auto-BP learns to map any given pattern to itself. During the testing phase, the CSNN is applied to new cases. The CSNN weights remain fixed (as determined by auto-BP) but the activation levels of the nodes are modified iteratively to optimize a goodness function. The medical findings act as the external inputs to the corresponding nodes. For every test case, CSNN tries to reconstruct the diagnosis nodes given the network constraints and the external inputs to the network. The activation levels achieved by the target nodes are used as decision variables for further analysis. Our CSNN was successfully applied on 500 patients with biopsy confirmed diagnosis. The CSNN was also used as an associative memory simulating dynamic scenarios for prototype analysis in our database.