In this study, abnormalities in medical images are analysed using three classifiers, and the results are compared. Breast cancer remains a major public health problem among women worldwide. Recently, many algorithms have evolved for the investigation of breast cancer diagnosis through medical imaging. A computer‐aided microcalcification detection method is proposed to categorise the nature of breast cancer as either benign or malignant from input mammogram images. The standard mammogram image corpus, the Mammogram Image Analysis Society database is utilised, and feature extraction is performed using five different wavelet families at level 4 and level 6 decomposition. The work is accomplished through firefly algorithm (FA), extreme learning machine (ELM) and least‐square‐based non‐linear regression (LSNLR) classifiers. The performance of the classifiers is compared by benchmark metrics, such as total error rate, specificity, sensitivity, area under the receiver operating characteristic curve, precision, F1 score and the Matthews correlation coefficient. As validation of the classifier results, a kappa analysis is included to determine the agreement among classifiers. The LSNLR classifier attains a 3% to 7% improvement in average accuracy compared with the average classification accuracy of the FA (86.75%) and ELM (90.836%) classifiers.

中文翻译：

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萤火虫算法、极限学习机和非线性回归模型对数字乳房 X 光照片微钙化的检测和分类：比较

在这项研究中，使用三个分类器分析医学图像中的异常，并比较结果。乳腺癌仍然是全世界女性的主要公共卫生问题。最近，许多算法已经发展用于通过医学成像研究乳腺癌诊断。提出了一种计算机辅助微钙化检测方法，用于根据输入的乳房 X 光照片图像将乳腺癌的性质分类为良性或恶性。使用标准的乳房 X 线图像语料库，乳房 X 线图像分析协会数据库，并使用五个不同的小波族在 4 级和 6 级分解中进行特征提取。这项工作是通过萤火虫算法 (FA)、极限学习机 (ELM) 和基于最小二乘法的非线性回归 (LSNLR) 分类器完成的。分类器的性能通过基准指标进行比较，例如总错误率、特异性、灵敏度、接收者操作特征曲线下的面积、精度、F1 分数和 Matthews 相关系数。作为分类器结果的验证，包括 kappa 分析以确定分类器之间的一致性。与 FA (86.75%) 和 ELM (90.836%) 分类器的平均分类精度相比，LSNLR 分类器的平均精度提高了 3% 到 7%。