Skin cancer is considered as a well-known type of cancer globally, and its occurrence has been found to be raised in current days. Researchers state that the disease requires early prediction so that the identification of precise signs will make it simple for the dermatologists and clinicians. This disorder has been established to be unpredictable. Hence, this paper intends to develop an efficient skin cancer detection scheme, which classifies the nature of cancer, whether it is normal, benign or malignant. Accordingly, the skin image which is given as input is segmented using k-means clustering model and the features are extracted from segmented image using Local Vector Pattern (LVP). Moreover, the extracted features are subjected to fuzzy classifier for recognizing the cancer. In addition, the limits of membership functions are optimally selected by improved Whale Optimization Algorithm (WOA). Thus, the proposed scheme is termed as Improved Selection of Encircling and Spiral updating position of WO-based Fuzzy Classifier (ISESW-FC). From the optimized output, the type of skin cancer image can be determined, whether it is normal, benign or malignant. The performance of proposed model is compared over other conventional methods, and its efficiency is proved by means of Type I and Type II measures.

中文翻译：

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使用改进的鲸鱼优化技术，通过自适应模糊分类器检测皮肤癌。

皮肤癌被认为是全球范围内众所周知的癌症类型，并且发现它的发生率在当今已经上升。研究人员指出，这种疾病需要及早进行预测，以便准确的迹象识别将使皮肤科医生和临床医生很容易做到。已经确定这种疾病是不可预测的。因此，本文打算开发一种有效的皮肤癌检测方案，该方案将癌症的性质分类为正常，良性还是恶性。因此，使用k均值聚类模型对作为输入给出的皮肤图像进行分割，并使用局部矢量模式（LVP）从分割后的图像中提取特征。此外，对提取的特征进行模糊分类以识别癌症。此外，通过改进的鲸鱼优化算法（WOA）最优选择隶属函数的极限。因此，所提出的方案被称为基于WO的模糊分类器（ISESW-FC）的环绕和螺旋更新位置的改进选择。从优化的输出，可以确定皮肤癌图像的类型，无论是正常，良性还是恶性的。将所提模型的性能与其他常规方法进行了比较，并通过I型和II型措施证明了其效率。