Breast cancer has been identified as one of the major diseases that have led to the death of women in recent decades. Mammograms are extensively used by physicians to diagnose breast cancer. The selection of appropriate image enhancement, segmentation, feature extraction, feature selection and prediction algorithm plays an essential role in precise cancer diagnosis on mammograms and remains as a major task in the research field. Classification methods predict the class label for unlabeled dataset based on its proximity to the learnt pattern. The selected features obtained after feature selection are classified using an extreme learning machines (ELM) to three classes with the classes being normal, benign and malignant. Low generalisation performance is the problem which happens due to the ill-conditioned output matrix of the hidden layer of the classifier. The optimisation algorithms would resolve these issues because of their global searching ability. This paper proposes ELM with Fruitfly Optimisation Algorithm (ELM-FOA) to tune the input weight to obtain optimum output at the ELM’s hidden node to obtain the solution analytically. The testing sensitivity and precision of ELM-FOA are 97.5% and 100% respectively. The developed method can detect the calcifications and tumours with 99.04% accuracy. The optimal selection of preprocessing and segmentation algorithms, features from multiple feature filters and the efficient classifier algorithm meliorate the performance of the approach.

乳腺癌已被确认为是近几十年来导致妇女死亡的主要疾病之一。乳房X线照片被医生广泛用于诊断乳腺癌。适当的图像增强，分割，特征提取，特征选择和预测算法的选择在乳房X线照片的精确癌症诊断中起着至关重要的作用，并且仍然是研究领域的主要任务。分类方法基于未标记数据集与学习模式的接近程度来预测其类别标签。使用极端学习机（ELM）将特征选择后获得的选定特征分类为三个类别，分别为正常，良性和恶性。低泛化性能是由于分类器隐藏层的条件不好的输出矩阵而发生的问题。由于其全局搜索能力，优化算法将解决这些问题。本文提出了采用Fruitfly优化算法（ELM-FOA）的ELM来调整输入权重，以在ELM的隐藏节点上获得最佳输出，从而获得解析结果。ELM-FOA的测试灵敏度和精度分别为97.5％和100％。所开发的方法能够以99.04％的准确度检测钙化和肿瘤。预处理和分割算法的最佳选择，来自多个特征过滤器的特征以及有效的分类器算法可改善该方法的性能。由于其全局搜索能力，优化算法将解决这些问题。本文提出了采用Fruitfly优化算法（ELM-FOA）的ELM来调整输入权重，以在ELM的隐藏节点上获得最佳输出，从而获得解析结果。ELM-FOA的测试灵敏度和精度分别为97.5％和100％。所开发的方法能够以99.04％的准确度检测钙化和肿瘤。预处理和分割算法的最佳选择，来自多个特征过滤器的特征以及有效的分类器算法可改善该方法的性能。由于其全局搜索能力，优化算法将解决这些问题。本文提出了采用Fruitfly优化算法（ELM-FOA）的ELM来调整输入权重，以在ELM的隐藏节点上获得最佳输出，从而获得解析结果。ELM-FOA的测试灵敏度和精度分别为97.5％和100％。所开发的方法能够以99.04％的准确度检测钙化和肿瘤。预处理和分割算法的最佳选择，来自多个特征过滤器的特征以及有效的分类器算法可改善该方法的性能。ELM-FOA的测试灵敏度和精度分别为97.5％和100％。所开发的方法能够以99.04％的准确度检测钙化和肿瘤。预处理和分割算法的最佳选择，来自多个特征过滤器的特征以及有效的分类器算法可改善该方法的性能。ELM-FOA的测试灵敏度和精度分别为97.5％和100％。所开发的方法能够以99.04％的准确度检测钙化和肿瘤。预处理和分割算法的最佳选择，来自多个特征过滤器的特征以及有效的分类器算法可改善该方法的性能。