Diagnosing tumor at early stages is very critical for treatment and understanding the disease pathogenesis. Analyzing and detecting tumor earlier helps us in deciding the course of actions for therapy. But analyzing multimodal cancer data and its sub-types is a very difficult and sensitive task and need classification methods with high accuracy. Also the data may be nonlinear and time sensitive. Hence a higher order neural network models called Pi-Sigma artificial neural network and functional link artificial neural network based on glowworm swarm optimization algorithm is proposed for dealing with multimodal cancer data. The performance of the proposed methods is tested with data from publicly available domains and the results show that the higher order neural networks with glowworm swarm optimization performs better than conventional neural network.