Determining the asymmetry of skin lesion with fuzzy borders.
It is highly desirable to identify malignant melanoma, a common cancer, at an early stage. One important clinical feature of this cancer is asymmetrical skin lesions. In this paper, we propose an adaptive fuzzy approach that uses symmetric distance (SD) to measure lesions with fuzzy borders. The use of a number of SD variations and the adoption of a backpropagation neural network enhances the discriminative power of the approach. Digitized images from the Lesion Clinic in Vancouver, Canada, demonstrate the accurate classification of asymmetric lesions at around 80%.
Ng
Vincent T Y
VT
Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong. cstyng@comp.polyu.edu.hk
Fung
Benny Y M
BY
Lee
Tim K
TK
D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D006801:Humans; D008545:Melanoma / Q000175:diagnosis*; D016571:Neural Networks, Computer; D009506:Nevus / Q000175:diagnosis*; D012878:Skin Neoplasms / Q000175:diagnosis*