Early diagnosis of malignant skin cancer from images is a significant part of the cancer treatment process. One of the principal purposes of this research is to propose a pipeline methodology for an optimum computer-aided diagnosis of skin cancers. The method contains four main stages. The first stage is to perform a preprocessing based on noise reduction and contrast enhancement. The second stage is to segment the region of interest (ROI). This study uses kernel fuzzy C-means for ROI segmentation. Then, some features from the ROI are extracted, and then, a feature selection is used for selecting the best ones. The selected features are then injected into a support vector machine (SVM) for final identification. One important part of the contribution in this study is to propose a developed version of a new metaheuristic, named neural network optimization algorithm, to optimize both parts of feature selection and SVM classifier. Comparison results of the method with 5 state-of-the-art methods showed the approach’s higher superiority toward the others.

中文翻译：

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使用内核模糊 C 均值和改进的神经网络优化算法检测皮肤癌


从图像中早期诊断恶性皮肤癌是癌症治疗过程的重要组成部分。这项研究的主要目的之一是提出一种最佳计算机辅助皮肤癌诊断的流程方法。该方法包含四个主要阶段。第一阶段是执行基于降噪和对比度增强的预处理。第二阶段是分割感兴趣区域（ROI）。本研究使用核模糊 C 均值进行 ROI 分割。然后，从 ROI 中提取一些特征，然后使用特征选择来选择最好的特征。然后将选定的特征注入到支持向量机（SVM）中进行最终识别。本研究贡献的一个重要部分是提出了一种新的元启发式算法的开发版本，称为神经网络优化算法，以优化特征选择和 SVM 分类器的两个部分。该方法与 5 种最先进方法的比较结果表明该方法相对于其他方法具有更高的优越性。