Skin Cancer Diagnosis using Hierarchical Neural Networks and Fuzzy Systems

Abstract

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.

Department(s)

Computer Science

Second Department

Nuclear Engineering and Radiation Science

Third Department

Chemistry

Fourth Department

Electrical and Computer Engineering

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 The Authors, All rights reserved.

Publication Date

01 Dec 1994

This document is currently not available here.

Share

 
COinS