Skin Cancer Diagnosis using Hierarchical Neural Networks and Fuzzy Systems
Skin cancers of all types comprise the largest group of cancers in the U.S. This group accounts for 1% of all cancer deaths. Fortunately, even the deadliest form of skin cancer can be treated successfully if detected in its early stage. In this study, we present a diagnostic-tree based hierarchical neural network system (HNN) which is integrated with a fuzzy system for the classification of four classes of skin tumors. These classes are: malignant melanoma, atypical mole, basal cell carcinoma or actinic keratosis, and intradermal nevus or seborrheic keratosis. The hierarchical neural network system is the integration of four distinct neural networks which are trained separately using the backpropagation learning algorithm. Results obtained through the HNN and fuzzy systems combined were significantly better than those produced by a straightforward neural network.
F. Erçal et al., "Skin Cancer Diagnosis using Hierarchical Neural Networks and Fuzzy Systems," Proceedings of the Artificial Neural Networks in Engineering Conference (1994, St. Louis, MO), pp. 613-618, American Society of Mechanical Engineers (ASME), Nov 1994.
Artificial Neural Networks in Engineering Conference (1994: Nov. 13-16, St. Louis, MO)
Electrical and Computer Engineering
Keywords and Phrases
Algorithms; Backpropagation; Computer Aided Diagnosis; Computer Simulation; Diseases; Fuzzy Sets; Hierarchical Systems; Image Processing; Learning Systems; Trees (Mathematics); Actinic Keratosis; Atypical Mole; Basal Cell Carcinoma; Fuzzy Systems; Hierarchical Neural Networks; Intradermal Nevus; Malignant Melanoma; Seborrhoic Keratosis; Skin Cancer Diagnosis; Skin Tumors; Neural Networks
International Standard Book Number (ISBN)
Article - Conference proceedings
© 1994 American Society of Mechanical Engineers (ASME), All rights reserved.