Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Artificial convolution neural network for medical image pattern recognition
Neural Networks - Special issue: automatic target recognition
Convolution neural network architecture with application for lung nodule detection in digital chest radiography
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Three neural network models were employed to evaluate theirperformances in the recognition of medical image patterns associatedwith lung cancer and breast cancer in radiography. The first methodwas a pattern match neural network. The second was a conventionalbackpropagation neural network. The third method was abackpropagation trained neocognitron in which the signal propagationis operated with the convolution calculation from one layer to thenext. In the convolution neural network (CNN) experiment, severaloutput association methods and trainer imposed driving functions inconjunction with the convolution neural network are proposed forgeneral medical image pattern recognition. An unconventional methodof applying rotation and shift invariance is also used to enhance theperformance of the neural nets.We have tested these methods for the detection of microcalcificationson mammograms and lung nodules on chest radiographs. Pre-scanmethods were previously described in our early publications. Theartificial neural networks act as final detection classifiers todetermine if a disease pattern is presented on the suspected imagearea. We found that the convolution neural network, which internallyperforms feature extraction and classification, achieves the bestperformance among the three neural network models. These resultsshow that some processing associated with disease feature extractionis a necessary step before a classifier can make an accuratedetermination.