Source:http://linkedlifedata.com/resource/pubmed/id/15493696
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
10
|
| pubmed:dateCreated |
2004-10-20
|
| pubmed:abstractText |
A computer-aided diagnosis (CAD) algorithm identifying breast nodule malignancy using multiple ultrasonography (US) features and artificial neural network (ANN) classifier was developed from a database of 584 histologically confirmed cases containing 300 benign and 284 malignant breast nodules. The features determining whether a breast nodule is benign or malignant were extracted from US images through digital image processing with a relatively simple segmentation algorithm applied to the manually preselected region of interest. An ANN then distinguished malignant nodules in US images based on five morphological features representing the shape, edge characteristics, and darkness of a nodule. The structure of ANN was selected using k-fold cross-validation method with k = 10. The ANN trained with randomly selected half of breast nodule images showed the normalized area under the receiver operating characteristic curve of 0.95. With the trained ANN, 53.3% of biopsies on benign nodules can be avoided with 99.3% sensitivity. Performance of the developed classifier was reexamined with new US mass images in the generalized patient population of total 266 (167 benign and 99 malignant) cases. The developed CAD algorithm has the potential to increase the specificity of US for characterization of breast lesions.
|
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:status |
MEDLINE
|
| pubmed:month |
Oct
|
| pubmed:issn |
0278-0062
|
| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:volume |
23
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
1292-300
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| pubmed:dateRevised |
2006-11-15
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| pubmed:meshHeading |
pubmed-meshheading:15493696-Adolescent,
pubmed-meshheading:15493696-Adult,
pubmed-meshheading:15493696-Aged,
pubmed-meshheading:15493696-Aged, 80 and over,
pubmed-meshheading:15493696-Algorithms,
pubmed-meshheading:15493696-Artificial Intelligence,
pubmed-meshheading:15493696-Breast Neoplasms,
pubmed-meshheading:15493696-Cluster Analysis,
pubmed-meshheading:15493696-Computer Graphics,
pubmed-meshheading:15493696-Decision Support Systems, Clinical,
pubmed-meshheading:15493696-Female,
pubmed-meshheading:15493696-Humans,
pubmed-meshheading:15493696-Image Enhancement,
pubmed-meshheading:15493696-Image Interpretation, Computer-Assisted,
pubmed-meshheading:15493696-Information Storage and Retrieval,
pubmed-meshheading:15493696-Neural Networks (Computer),
pubmed-meshheading:15493696-Numerical Analysis, Computer-Assisted,
pubmed-meshheading:15493696-Pattern Recognition, Automated,
pubmed-meshheading:15493696-Reproducibility of Results,
pubmed-meshheading:15493696-Sensitivity and Specificity,
pubmed-meshheading:15493696-Signal Processing, Computer-Assisted,
pubmed-meshheading:15493696-Subtraction Technique,
pubmed-meshheading:15493696-Ultrasonography, Mammary,
pubmed-meshheading:15493696-User-Computer Interface
|
| pubmed:year |
2004
|
| pubmed:articleTitle |
Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features.
|
| pubmed:affiliation |
Interdisciplinary Program-Biomedical Engineering, Seoul National University, Seoul 110-744, Korea.
|
| pubmed:publicationType |
Journal Article,
Comparative Study,
Research Support, Non-U.S. Gov't,
Validation Studies
|