Content-based image classification using a neural network
Pattern Recognition Letters
An effective learning of neural network by using RFBP learning algorithm
Information Sciences—Informatics and Computer Science: An International Journal
RBF-based neurodynamic nearest neighbor classification in real pattern space
Pattern Recognition
Computer Methods and Programs in Biomedicine
Degree prediction of malignancy in brain glioma using support vector machines
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A bidirectional heteroassociative memory for binary and grey-level patterns
IEEE Transactions on Neural Networks
Real-time learning capability of neural networks
IEEE Transactions on Neural Networks
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Image classification is one of the typical computational applications widely used in the medical field especially for abnormality detection in Magnetic Resonance (MR) brain images. The automated image classification systems used for such applications must be significantly efficient in terms of accuracy since false detection may lead to fatal results. Another requirement is the high convergence rate which accounts for the practical feasibility of the system. Among the automated systems, Artificial Neural Network (ANN) is gaining significant positions for solving computational problems. Besides multiple advantages, there are also few drawbacks associated with the neural networks which are unnoticed for most of the applications. The main drawback is that the ANN which yields high accuracy requires high convergence time period and the ANN which are much quicker are usually inaccurate. Hence, there is a significant necessity for ANN which satisfies the criteria of high convergence rate and accuracy simultaneously. In this work, this drawback is tackled by proposing two novel neural networks namely Modified Counter Propagation Neural Network (MCPN) and Modified Kohonen Neural Network (MKNN). These networks are framed by performing modifications in the training methodology of conventional CPN and Kohonen networks. The main concept of this work is to make the ANN iteration-free which ultimately improves the convergence rate besides yielding accurate results. The performance of these networks are analysed in the context of abnormal brain image classification. Experimental results show promising results for the proposed networks in terms of the performance measures.