Ng Vincent T Y, Fung Benny Y M, Lee Tim K
Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong.
Comput Biol Med. 2005 Feb;35(2):103-20. doi: 10.1016/j.compbiomed.2003.11.004.
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%.
尽早识别恶性黑色素瘤这种常见癌症是非常有必要的。这种癌症的一个重要临床特征是皮肤病变不对称。在本文中，我们提出了一种自适应模糊方法，该方法使用对称距离（SD）来测量具有模糊边界的病变。通过使用多种SD变体并采用反向传播神经网络，增强了该方法的判别能力。来自加拿大温哥华病变诊所的数字化图像显示，不对称病变的准确分类率约为80%。
