A neural network approach for the automatic detection of microaneurysms in retinal angiograms

In this paper a neural network structure is used to develop a system capable of detecting microaneurysms locations in retinal angiograms. The LVQ (learning vector quantization) neural network is used to classify the input patterns into their desired classes using competitive layers. The neurons in the competitive layers compete among each other to produce subclasses. These subclasses are then combined to produce the desired output classes. The input vector of the neural network is derived from a grid of smaller image windows. The presence of microaneurysms in these windows is detected according to a novel multi-stage training procedure that has proved to be very effective.