Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection
Pattern Recognition Letters
IEEE Transactions on Information Technology in Biomedicine
An automatic microcalcification detection system based on a hybrid neural network classifier
Artificial Intelligence in Medicine
Self-organizing map for cluster analysis of a breast cancer database
Artificial Intelligence in Medicine
Fast training of multilayer perceptrons
IEEE Transactions on Neural Networks
Impact of multiple clusters on neural classification of ROIs in digital mammograms
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Diagnosing Breast Masses in Digital Mammography Using Feature Selection and Ensemble Methods
Journal of Medical Systems
Fast opposite weight learning rules with application in breast cancer diagnosis
Computers in Biology and Medicine
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Objective: The main objective of this paper is to present a novel learning algorithm for the classification of mass abnormalities in digitized mammograms. Methods and material: The proposed approach consists of new network architecture and a new learning algorithm. The original idea is based on the introduction of an additional neuron in the hidden layer for each output class. The additional neurons for benign and malignant classes help in improving memorization ability without destroying the generalization ability of the network. The training is conducted by combining minimal distance-based similarity/random weights and direct calculation of output weights. Results: The proposed approach can memorize training patterns with 100% retrieval accuracy as well as achieve high generalization accuracy for patterns which it has never seen before. The grey-level and breast imaging reporting and data system-based features from digitized mammograms are extracted and used to train the network with the proposed architecture and learning algorithm. The best results achieved by using the proposed approach are 100% on training set and 94% on test set. Conclusion: The proposed approach produced very promising results. It has outperformed existing classification approaches in terms of classification accuracy, generalization and memorization abilities, number of iterations, and guaranteed training on a benchmark database.