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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
2
|
| pubmed:dateCreated |
1992-2-21
|
| pubmed:abstractText |
We have developed a computerized system that can aid in the radiologist's diagnosis in the detection and classification of coronary artery diseases. The technique employs a neural network to analyze 201Tl myocardial SPECT bull's-eye images. This multi-layer feed-forward neural network with a backpropagation algorithm has 256 input units (pattern: compressed 16 x 16-matrix images), 5-140 units in a single hidden layer, and eight output units (diagnosis: one normal and seven different types of abnormalities). The neural network was taught using pairs of training (learning) input data (bull's-eye "EXTENT" image) and desired output data ("correct" diagnosis). The effects of the numbers of hidden units and learning iterations in the network on the recognition performance were examined. In our initial stage, the results show that the recognition performance of the neural network is better than that of the radiology resident but worse than that of the experienced radiologist. Our study also demonstrates that the result produced in the neural network depends on the variety of the training examples used. The preliminary study suggests that the neural network approach is useful for the computer-aided diagnosis of coronary artery diseases in myocardial SPECT bull's-eye images.
|
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:status |
MEDLINE
|
| pubmed:month |
Feb
|
| pubmed:issn |
0161-5505
|
| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:volume |
33
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
272-6
|
| pubmed:dateRevised |
2000-12-18
|
| pubmed:meshHeading | |
| pubmed:year |
1992
|
| pubmed:articleTitle |
Application of artificial neural network to computer-aided diagnosis of coronary artery disease in myocardial SPECT bull's-eye images.
|
| pubmed:affiliation |
Department of Electronics and Computer Engineering, Gifu University, Japan.
|
| pubmed:publicationType |
Journal Article
|