In this paper, an essential system to identify the melanoma in skin at an early stage is proposed. Skin Cancer (SC) is one of the deadliest disease and its morality rates is very high. A SC classification model is designed based on the novel Color, Shape feature extraction and Classifier to detect the Melanoma which is known as CSC-Mel identification model. In preprocessing, feature and gradient adaptive of contour model is employed to segment the skin lesion. Along with ABCD rule, a novel shape and colour features are extracted as features and K-Nearest Neighbor (KNN) classification is employed for the classification. The CSC-Mel Identification Model is tested on PH2 dermoscopic image dataset with 3-Fold Cross Validation (FCV) for testing and development process. Results shows that the CSC-Mel identification model identifies the skin cancer with an accuracy of 90.5%.

本文提出了一种早期识别皮肤中黑色素瘤的基本系统。皮肤癌（SC）是最致命的疾病之一，其道德率很高。基于新颖的颜色，形状特征提取和分类器设计了SC分类模型以检测黑素瘤，这被称为CSC-Mel识别模型。在预处理中，采用轮廓模型的特征和梯度自适应来分割皮肤病变。连同ABCD规则一起，提取一种新颖的形状和颜色特征作为特征，并使用K最近邻（KNN）分类进行分类。在具有2折交叉验证（FCV）的PH2皮肤镜图像数据集上测试CSC-Mel识别模型，以进行测试和开发过程。