Abstract
The use of neural networks for automatic identification of variegated coloring, which is believed to be one of the most predictive features for malignant melanoma, is described. The Nestor development system (NDS) was chosen for neural network implementation. At the heart of NDS is a three-layer neural network called a restricted Coulomb energy (RCE) network. The learning scheme and the database for detection of variegated coloring are discussed. Results are reported
Recommended Citation
A. Durg et al., "Identification of Variegated Coloring in Skin Tumors: Neural Network vs. Rule-Based Induction Methods," IEEE Engineering in Medicine and Biology Magazine, vol. 12, no. 3, pp. 71 - 74, Institute of Electrical and Electronics Engineers (IEEE), Sep 1993.
The definitive version is available at https://doi.org/10.1109/51.232345
Department(s)
Chemistry
Second Department
Electrical and Computer Engineering
Keywords and Phrases
3-Layer Neural Network; Nestor Development System; Colour; Database; Learning Scheme; Malignant Melanoma Predictive Features; Medical Diagnosis; Medical Image Processing; Neural Nets; Restricted Coulomb Energy Network; Rule-Based Induction Methods; Skin; Variegated Coloring Identification
International Standard Serial Number (ISSN)
0739-5175
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 1993 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Sep 1993
