Improved neural network-based interpretation of colonoscopy images through on-line learning and evolution
George D. Magoulas, Vassilis P. Plagianakos, Michael N. Vrahatis · 2001
In this work we explore on-line training of neural networks for interpreting colonoscopy images through tracking the changing location of an approximate solution of a pattern-based, and, thus, dynamically changing, error function. We have developed a memory-based adaptation of the learning rate for the on-line Backpropagation (BP) and we investigate the use of this scheme in an on-line evolution process that applies an on-line BP-seeded Differential Evolution Strategy to (re-)adapt the neural network to modified environmental conditions. We compare this hybrid strategy to other standard training methods that have traditionally been used for training neural networks off-line. Preliminary results in interpreting colonoscopy images and frames of video sequences suggest that networks trained with this strategy detect malignant regions of interest with high accuracy. Extensive testing in interpreting more complex regions is necessary to fully investigate the properties, the effect of the heuristic parameters and the performance of the hybrid learning strategy in this context.