Name
Abstract | ||
|---|---|---|
Malignant melanoma is a popular cancer among youth; it is desirable to have a fast and convenience way to determine this disease in its early stage. One of the clinical features in diagnosis is related to the shape of lesions. In previous studies, circularity is commonly usedas the asymmetric measurement of skin lesions. However, this measurement depends very much on the accuracy of the segmentation result. In this paper, we present an artificial neural network model to improve the measurements of the asymmetries of lesions that may havefuzzy borders. The main idea is enhancing the symmetric distant (eSD) with a number of variations. Results from experiments, which use the digitized images from the Lesion Clinic in Vancouver, Canada have shown the good discriminating power of the neural network model. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.compbiomed.2003.11.004 | Computers in Biology and Medicine |
Keywords | DocType | Volume |
main idea,skin lesion,malignant melanoma,skin lesions,lesion clinic,asymmetry measurement,neural network model,clinical feature,artificial neural network model,melanoma,fuzzy borders,popular cancer,asymmetric measurement,early stage,medical imaging,digitized image,artificial neural network,digital image,backpropagation,neural nets,electrostatic discharge,shape,measurement errors,artificial neural networks,image segmentation,symmetry,cancer,skin | Journal | 35 |
Issue | ISSN | ISBN |
2 | Computers in Biology and Medicine | 0-7695-1907-5 |
Citations | PageRank | References |
1 | 0.42 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vincent T. Y. Ng | 1 | 504 | 122.85 |
| Tim Lee | 2 | 1 | 0.42 |
| Benny Y. M. Fung | 3 | 1 | 0.76 |