Mammography is a significant screening test for early detection of breast cancer, which increases the patient’s chances of complete recovery. In this paper, a clustering method is presented for the detection of breast cancer tumor locations and areas. To implement the clustering method, we used the growth region approach. This method detects similar pixels nearby. To find the best initial point for detection, it is essential to remove human interaction in clustering. Therefore, in this paper, the FCM-GA algorithm is used to find the best point for starting growth. Their results are compared with the manual selection method and Gaussian Mixture Model method for verification. The classification is performed to diagnose breast cancer type in two primary datasets of MIAS and BI-RADS using features of GLCM and probabilistic neural network (PNN). Results of clustering show that the presented FCM-GA method outperforms other methods. Moreover, the accuracy of the clustering method for FCM-GA is 94%, as the best approach used in this paper. Furthermore, the result shows that the PNN methods have high accuracy and sensitivity with the MIAS dataset.

中文翻译：

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使用生长区域方法和概率神经网络介绍用于检测和分类乳腺癌的新型混合算法

乳房 X 光检查是早期检测乳腺癌的重要筛查测试，可增加患者完全康复的机会。在本文中，提出了一种用于检测乳腺癌肿瘤位置和区域的聚类方法。为了实现聚类方法，我们使用了增长区域方法。该方法检测附近的相似像素。为了找到最佳的检测初始点，必须去除聚类中的人为交互。因此，本文采用FCM-GA算法寻找开始增长的最佳点。他们的结果与手动选择方法和高斯混合模型方法进行比较以进行验证。使用 GLCM 和概率神经网络 (PNN) 的特征，在 MIAS 和 BI-RADS 的两个主要数据集中进行分类以诊断乳腺癌类型。聚类结果表明，所提出的 FCM-GA 方法优于其他方法。此外，FCM-GA 聚类方法的准确率为 94%，是本文使用的最佳方法。此外，结果表明 PNN 方法对 MIAS 数据集具有较高的准确性和敏感性。