基于中性c均值和模糊c均值聚类算法的皮肤癌检测

A. Abdelhafeez, Hoda K. Mohamed
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引用次数: 2

摘要

黑色素瘤是一种对人的生命构成最大风险的皮肤癌,在皮肤癌疾病中死亡率最高。即便如此,由于缺乏对比黑色素瘤的摩尔浓度和皮肤分数,以及黑色素瘤受影响和未受影响区域之间更大程度的颜色相似性,在初始阶段自动定位和分类皮肤病变仍然是一项复杂的任务。当代技术的进步和研究方法使它能够更成功地识别和区分这种类型的皮肤癌。本研究提出了一种称为中性粒细胞c均值聚类(NCMC)的聚类技术,以对皮肤癌检测中的模糊数据进行分组。该算法从模糊c均值和中性集结构中获取线索。为了达到这样的结构,必须首先创建一个适当的目标函数,然后最小化。然后必须将聚类问题表述为受限最小化问题,其解决方案由目标函数决定。本文将NCMC与模糊c均值聚类(FCMC)进行了比较。结果表明,NCMC比FCMC更合适。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms
Melanoma is the kind of skin cancer that poses the greatest risk to one's life and has the maximum mortality rate within the group of skin cancer disorders. Even so, the automated placement and classification of skin lesions at initial phases remains a complicated task due to the lack of contrast melanoma molarity and skin fraction and a greater level of color similarity among melanoma-affected and -nonaffected areas. Contemporary technological improvements and research methods enabled it to recognize and distinguish this type of skin cancer more successfully. A clustering technique called neutrosophic c-means clustering (NCMC) is presented in this research to group ambiguous data in the detection of skin cancer. This algorithm takes its cues from both fuzzy c-means and the neutrosophic set structure. To arrive at such a structure, an appropriate objective function must first be created and then minimized. The clustering issue must then be stated as a restricted minimization problem, the solution of which is determined by the objective function. This paper made a comparison between NCMC and fuzzy c-means clustering (FCMC). The results show that the NCMC is more suitable than the FCMC.
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CiteScore
1.70
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