Detection of breast cancer from histopathology image and classifying benign and malignant state using fuzzy logic
Detection of breast cancer from histopathology image and classifying benign and malignant state using fuzzy logic
复制标题
从组织病理学图像检测乳腺癌并使用模糊逻辑对良性和恶性状态进行分类
DOI：
10.1109/ceeict.2016.7873137
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Md. Maruf Hossain Shuvo
中科院分区：
文献类型：
--
作者：
F. Johra;Md. Maruf Hossain Shuvo
乳腺癌是全世界妇女面临的主要公共卫生问题之一。它有两种状态，称为良性和恶性。良性状态生长缓慢，很少扩散到身体的其他部位，并且有明确的边界。另一方面，恶性状态有增长更快的趋势，并且危及生命。因此，这两种状态的分类对于正确诊断乳腺癌患者至关重要。在本文中，我们介绍了一个新的管道乳腺癌细胞检测和特征提取使用开源图像分析软件CellProfiler。提出了一种基于模糊推理系统的良恶性状态分类算法。使用众所周知的性能参数，如准确性，灵敏度和特异性的比较表明，我们提出的方法比人工神经网络（ANN）和支持向量机（SVM）的分类性能更好。该方法的敏感性、特异性和准确性分别为95.6%、90.63%和94.26%。
Breast cancer is one of the major public health problem for women throughout the world. It has two states, known as benign and malignant. Benign state is slow growing, rarely spread to other parts of body and have well-defined borders. On the other hand, Malignant state has tendency to grow faster and it is life threatening. So, classification of this two state is crucial for proper diagnosis of a breast cancer patient. In this paper, we have introduced a new pipeline for breast cancer cell detection and feature extraction using an open source image analysis software named CellProfiler. We proposed an algorithm based on fuzzy inference system for classification of the benign and malignant state. Comparison using well known performance parameters such as accuracy, sensitivity and specificity shows that our proposed approach performs better than the Artificial Neural Network (ANN) and Support Vector Machine (SVM) based classification. The sensitivity, specificity, and accuracy of the proposed method is 95.6%, 90.63%, and 94.26% respectively.