Breast cancer considered to be a significant health issue among women. Early detection will ensure the treatment is easier and more successful. Recently, numerous methodologies have developed using medical imaging to investigate breast cancer. This research seeks to build a computer-aided diagnostic (CAD) system to interpret mammograms. The first stage of CAD includes preprocessing, Fuzzy c means based segmentation applied to a localized area. In the second stage of the CAD method, the extraction of the feature is carried out using three distinct wavelet families with decomposition level at 4 and 6. The ANN, SVM, and ELM classifiers are used in the final stage to enable accurate classification. This article proposes ELM with the Grasshopper Optimization Algorithm (ELM-GOA) to adjust the weight between the input and hidden layer to obtain maximum performance at the middle layer. This method adopts mammogram enhancement, optimum image segmentation, wavelet-based feature extraction, and grasshopper optimization algorithm based ELM to ameliorating the accuracy and reducing the computational cost. The result shows that ELM-GOA has precision and sensitivity of 100% and 98% respectively. The CAD system can identify tumors with 99.33 % accuracy.

中文翻译：

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使用小波特征和优化分类器早期检测乳腺恶性肿瘤

乳腺癌被认为是女性的重要健康问题。及早发现将确保治疗更容易和更成功。最近，已经开发了许多使用医学成像来研究乳腺癌的方法。这项研究旨在建立一个计算机辅助诊断 (CAD) 系统来解释乳房 X 光照片。CAD 的第一阶段包括预处理、基于模糊 c 均值的应用于局部区域的分割。在 CAD 方法的第二阶段，使用分解级别为 4 和 6 的三个不同小波族进行特征提取。最后阶段使用 ANN、SVM 和 ELM 分类器以实现准确分类。本文提出 ELM 与 Grasshopper 优化算法 (ELM-GOA) 来调整输入层和隐藏层之间的权重，以获得中间层的最大性能。该方法采用乳腺X线增强、优化图像分割、基于小波的特征提取和基于ELM的蚱蜢优化算法来提高精度并降低计算成本。结果表明，ELM-GOA 的精度和灵敏度分别为 100% 和 98%。CAD 系统可以以 99.33% 的准确率识别肿瘤。结果表明，ELM-GOA 的精度和灵敏度分别为 100% 和 98%。CAD 系统可以以 99.33% 的准确率识别肿瘤。结果表明，ELM-GOA 的精度和灵敏度分别为 100% 和 98%。CAD 系统可以以 99.33% 的准确率识别肿瘤。