Neural network architectures in data sequence analysis: diagnosis of Reinke's oedema and Laryngeal polyps

Abstract

In this review paper, we focus our attention on presenting selected neural network architectures dedicated to the analysis of sequential data, in particular to support the diagnosis of Reinke's oedema and laryngeal polyps. The research discussed here is located in the area of clinical computer decision support systems (CDS) based on the use of artificial recurrent neural networks (RNNs) for speech signal analysis. RNNs are able to predict time series due to their memory and local recurrent connections. In the experimental part, Elman-Jordan artificial neural networks are used, whose characteristics are speed and accuracy in pattern learning allowing real-time decision-making. In the review presented here, one important theme is the use of Bezier curves for preprocessing the speech signal, leading to data reduction and noise elimination. Elman-Jordan networks significantly speed up the learning process and show high classification accuracy in laryngopathy diagnosis.