title: Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model.
abstract: We develop a novel method for classifying melanocytic tumors as benign or malignant by the analysis of digital dermoscopy images. The algorithm follows three steps: first, lesions are extracted using a self-generating neural network (SGNN); second, features descriptive of tumor color, texture and border are extracted; and third, lesion objects are classified using a classifier based on a neural network ensemble model. In clinical situations, lesions occur that are too large to be entirely contained within the dermoscopy image. To deal with this difficult presentation, new border features are proposed, which are able to effectively characterize border irregularities on both complete lesions and incomplete lesions. In our model, a network ensemble classifier is designed that combines back propagation (BP) neural networks with fuzzy neural networks to achieve improved performance. Experiments are carried out on two diverse dermoscopy databases that include images of both the xanthous and caucasian races. The results show that classification accuracy is greatly enhanced by the use of the new border features and the proposed classifier model.
authors.forename: Fengying
authors.initials: F
authors.lastname: Xie
authors.forename: Haidi
authors.initials: H
authors.lastname: Fan
authors.forename: Yang
authors.initials: Y
authors.lastname: Li
authors.forename: Zhiguo
authors.initials: Z
authors.lastname: Jiang
authors.forename: Rusong
authors.initials: R
authors.lastname: Meng
authors.forename: Alan
authors.initials: A
authors.lastname: Bovik
mesh_terms: D000465:Algorithms; D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000000981:diagnostic imaging*; D016571:Neural Networks, Computer; D012878:Skin Neoplasms / Q000000981:diagnostic imaging*