Department of Biomedical Engineering, Engineering Faculty, Dicle University, Diyarbakır, Turkey.
Med Sci Monit. 2018 Sep 17;24:6537-6543. doi: 10.12659/MSM.910520.
BACKGROUND Breast cancer is one of the most common cancer types in the world and is a serious threat to health. This type of cancer is complex; it is a hereditary disease and does not result from a single cause. The diagnosis of cancer starts with a biopsy. Various methods are used to detect and recognize cancer cells, from microscopic images and mammography to ultrasonography and magnetic resonance images (MRI). MATERIAL AND METHODS Detection and characterization of benign and malignant cells by image-processing-based segmentation for breast cancer diagnosis is important for early diagnosis. In the present study, Extreme Learning Machine (ELM) classification was performed for 9 features based on image segmentation in the Breast Cancer Wisconsin (Diagnostic) data set in the UC Irvine Machine Learning Repository database. RESULTS The results obtained with the developed method were compared with the results of other machine learning methods (Naive Bayes, Support Vector Machine, and Artificial Neural Network) and it showed the highest performance, with a result of 98.99%. CONCLUSIONS It was found that both accuracy and speed were good. We present a method that can be applied in cell morphology detection and classification in automated systems that classify by computer-aided mammogram image features.
乳腺癌是世界上最常见的癌症类型之一，严重威胁着人们的健康。这种癌症很复杂，它是一种遗传性疾病，不是由单一原因引起的。癌症的诊断始于活检。有多种方法可用于检测和识别癌细胞，从显微镜图像和乳房 X 光检查到超声检查和磁共振成像 (MRI)。
基于图像处理的分割的良性和恶性细胞的检测和特征化对于乳腺癌的早期诊断非常重要。在本研究中，在加利福尼亚大学欧文分校机器学习存储库数据库的乳腺肿瘤 Wisconsin (诊断) 数据集上，对基于图像分割的 9 个特征进行了极限学习机 (ELM) 分类。
与其他机器学习方法 (朴素贝叶斯、支持向量机和人工神经网络) 的结果进行比较，所开发的方法的结果显示出最高的性能，准确率达到 98.99%。
发现准确性和速度都很好。我们提出了一种方法，可应用于自动系统中的细胞形态检测和分类，该系统可通过计算机辅助乳房 X 光图像特征进行分类。
