Why the study?
Does a fully connected backpropagation neural network improve classifying performance compared to a patterned network in detecting cardiac diseases from echocardiographic images?
Does a fully connected backpropagation neural network improve classifying performance compared to a patterned network in detecting cardiac diseases from echocardiographic images?
A fully connected backpropagation neural network outperforms a patterned network in classifying cardiac diseases from two-dimensional echocardiographic images.
Supports fully connected networks for improved echocardiographic cardiac classification; extends neural network architecture comparisons in cardiology.
The usefulness of backpropagation neural networks in the analysis of two-dimensional echocardiographic (2DE) images has been evaluated. The gray-scale levels from 2DE images directly correspond to the intensity of echo signal from cardiac tissue, providing visual texture and allowing qualitative and quantitative analysis of myocardial tissue. A subject population consisting of 11 normal, 7 hypertrophic cardiomyopathy, and 11 myocardial infarction patients was studied. Two types of backpropagational neural networks were used: fully connected, and patterned. In the fully connected network, the outputs of neurodes in each layer are connected to the inputs of all neurodes in the following layer. In the patterned network, only neurodes within a defined neighborhood are connected. The results suggest that the fully connected network provides better classifying performance than the patterned network.
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Cios et al. (1990) studied this question.
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