Fast diagnosing of pediatric respiratory diseases by using high speed neural networks

In this paper, a new fast neural model for testing massive volume of medical data is presented. The idea is to accelerate the process of detecting and classifying pediatric respiratory diseases by using neural networks. This is done by applying cross correlation between the input patterns and the input weights of neural networks in the frequency domain rather than time domain. Furthermore, such model is very useful for understanding the internal relation between the medical patterns. In addition, the input patterns are collected in one vector and manipulated as a one pattern. Moreover, before training neural networks, rough sets are used to reduce the length of the feature input vector. The most important feature elements are used to train the neural networks. The reduced input medical patterns are classified to one of eight diseases. Simulation results confirm the theoretical considerations as 98% of all tested cases are classified correctly. The presented model can be applied successfully for any other classification application.