Artificial Neural Network (ANN) is the primary automated AI system preferred for medical applications. Even though ANN possesses multiple advantages, the convergence of the ANN is not always guaranteed for the practical applications. This often results in the local minima problem and ultimately yields inaccurate results. This convergence problem is common among ANNs and especially in Kohonen neural networks which employ unsupervised training methodology. In this work, an Efficient Kohonen Fuzzy Neural (EKFN) network is proposed to eliminate the iteration dependent nature of the conventional system. The suitability of this hybrid automated system is illustrated in the context of pathology identification in retinal images. This disease identification system includes anatomical structure segmentation from retinal images followed by image classification. The performance measures used are accuracy, sensitivity, specificity, positive predictive value and positive likelihood ratio. Experimental results show promising possibilities for the hybrid systems in terms of performance measures.
In this paper we introdiice a new approach to the preprocessing (initial setting) of weight vectors and thus a spoed-up of the well-knowri SOM (Kohonen’s, SOFM) neural network. The idea of the method (we call it Prep through this paper) consists in spreading a small lattice over the pattern space and consequently completing its inner meshes and boundaries to obtain a larger lattice. This large lattice is then tuned by its training for a short time. To justify the speed up of the Prep method we give a detailed time analysis. To demonstrate the suggested method we show its abilities on several representative examples.