The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.

中文翻译：

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使用直觉可能模糊C均值聚类和模糊SVM算法的医学图像有效分割和分类系统。

疱疹病毒，多瘤病毒，乳头瘤病毒和逆转录病毒家族与乳腺癌有关。需要更多的努力来评估这些病毒在女性乳腺癌病例的检测和诊断中的作用。本文的目的是在乳腺X射线摄影图像分析学会（MIAS）医学图像中提出一种有效的分割和分类系统。对于医学图像而言，分割变得具有挑战性，因为它们的照明方式不正确。分割的作用对于检测人类综合症至关重要。这项研究工作是基于直觉可能模糊c均值（IPFCM）聚类的医学图像分割。将直觉模糊c均值（IFCM）算法和可能的模糊c均值（PFCM）算法进行混合处理，以解决模糊c均值问题。本文介绍的聚类方法保留了PFCM的优点，这有助于克服重合的聚类问题，从而克服了噪声并降低了对异常值的敏感性。IPFCM通过使用直觉模糊集改善了模糊c均值的基础。对于用于乳腺异常图像检测器的乳腺X线照片图像的聚类，已经应用了IPFCM技术。将该方法与其他可用的模糊聚类方法进行了比较，以证明该方法的有效性。我们比较了支持向量机（SVM），决策树（DT），粗糙集数据分析（RSDA）和Fuzzy-SVM分类算法，以获得最佳分类结果。研究结果表明，所提出的方法在聚类和乳腺癌分类中均非常有效。对于不同噪声水平的IPFCM分别为5％，7％和9％的MIAS图像，其性能平均分割精度分别为91.25％，87.50％和85.30％。Fuzzy-SVM的方法（大津，模糊c均值，IFCM，PFCM和IPFCM）的平均分类准确率分别为79.69％，92.19％，93.13％，95.00％和98.85％。