Breast imaging technique called mammography has gained bigger attention among the researchers for the diagnosis of breast malignancy in the woman. Mammogram screening is the most effective procedure to visualize various potential problems in the breast. The two most common features connected with breast tumors are mass lesions and microcalcification. The collection of suitable image preprocessing, segmentation, feature extraction, selection and prediction algorithms play an essential role in the accurate detection and classification of cancer on mammograms. Classification techniques estimate unlabeled datasets class labeling depending on its similarity to the pattern learned. The Glowworm Swarm Optimization(GSO) algorithm is ideal for finding several solutions, and dissimilar or equivalent objective function values at the same time. This feature of GSO is useful for optimizing the feature set obtained from multiscale feature extraction procedures. Poor performance in generalization is the issue that arises due to the unconditioned output matrix of the hidden stage of the ELM classifier. The optimization algorithms will address this matter because of their global search capabilities. This article suggests ELM with the Fruitfly Optimization Algorithm (ELM‐FOA) along with GSO to regulate the input weight to achieve maximal performance at the hidden node of the ELM. The testing precision and sensitivity of GSO‐ELM‐FOA are 100% and 97.91%, respectively. The system developed will detect the calcifications and tumors with an accuracy of 99.15%.

中文翻译：

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使用混合极限学习机分类器从数字化乳腺X线照片检测和分类乳腺癌

在女性乳房恶性肿瘤的诊断研究人员中，被称为乳房X线照相术的乳房成像技术受到了越来越多的关注。乳房X光检查是最有效的方法，可以可视化乳房中的各种潜在问题。与乳腺肿瘤有关的两个最常见的特征是块状病变和微钙化。合适的图像预处理，分割，特征提取，选择和预测算法的集合在乳房X线照片上准确检测和分类癌症中起着至关重要的作用。分类技术根据其与学习模式的相似性来估计未标记的数据集的类别标记。萤火虫群优化算法（GSO）非常适合同时查找多个解决方案以及不相似或等效的目标函数值。GSO的此功能可用于优化从多尺度特征提取过程中获得的特征集。由于ELM分类器隐藏阶段的无条件输出矩阵，泛化性能差是一个问题。由于其全局搜索功能，优化算法将解决此问题。本文建议将ELM与Fruitfly优化算法（ELM‐FOA）以及GSO一起使用，以调节输入权重，以在ELM的隐藏节点上实现最佳性能。GSO‐ELM‐FOA的测试精度和灵敏度分别为100％和97.91％。开发的系统将以99.15％的准确度检测钙化和肿瘤。由于ELM分类器隐藏阶段的无条件输出矩阵，泛化性能差是一个问题。由于其全局搜索功能，优化算法将解决此问题。本文建议将ELM与Fruitfly优化算法（ELM‐FOA）以及GSO一起使用，以调节输入权重，以在ELM的隐藏节点上实现最佳性能。GSO‐ELM‐FOA的测试精度和灵敏度分别为100％和97.91％。开发的系统将以99.15％的准确度检测钙化和肿瘤。由于ELM分类器隐藏阶段的无条件输出矩阵，泛化性能差是一个问题。由于其全局搜索功能，优化算法将解决此问题。本文建议将ELM与Fruitfly优化算法（ELM‐FOA）以及GSO一起使用，以调节输入权重，以在ELM的隐藏节点上实现最佳性能。GSO‐ELM‐FOA的测试精度和灵敏度分别为100％和97.91％。开发的系统将以99.15％的准确度检测钙化和肿瘤。本文建议将ELM与Fruitfly优化算法（ELM‐FOA）以及GSO一起使用，以调节输入权重，以在ELM的隐藏节点上实现最佳性能。GSO‐ELM‐FOA的测试精度和灵敏度分别为100％和97.91％。开发的系统将以99.15％的准确度检测钙化和肿瘤。本文建议将ELM与Fruitfly优化算法（ELM-FOA）以及GSO一起使用，以调节输入权重，以在ELM的隐藏节点上实现最佳性能。GSO‐ELM‐FOA的测试精度和灵敏度分别为100％和97.91％。开发的系统将以99.15％的准确度检测钙化和肿瘤。