The survival rates of breast cancer are going up due to the emerging increase in its screening and diagnosis methods. However, breast cancer is yet the most intrusive disease found in women. Many techniques are emerging during recent years for the investigation of breast cancer using imaging modalities. The paper intends to categorize the severity present in the digital mammography images as either benign (B) or malignant (M) using an improved crow‐search optimization algorithm (ImCSOA). In the literature, the CSOA is generally used for solving several feature selection and numerical optimization problems. The objective is to utilize this popular optimization algorithm for the problem of biomedical image classification. However, if this algorithm is applied directly to classification problems, then it will result in poor classification of data. Hence, the original CSO (OCSO) algorithm undergoes suitable enhancements using a novel controlled parameter tuning, control operator and chaotic‐maps‐based controlled randomness. Four distinct chaotic maps are used for controlling the randomness in the OCSO algorithm. The mammogram images are obtained from the Mammographic Image Analysis Society and Digital Database for Screening Mammography data sets for the evaluation. The classification is accomplished through discrete wavelet transform‐based statistical features that are extracted at two levels [level 4 (L4) and level 6 (L6)] of decomposition. For both data sets, the ImCSOA with L4 and L6 decomposed bior4.4 wavelet features provides the maximum accuracy of around 85% to 86%, which is approximately 62% to 88% better than the OCSO algorithm with L4 and L6 decomposed bior4.4 wavelet features.

中文翻译：

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一种新颖的改进的crow-search算法，可对数字乳房X线照片中的严重性进行分类

乳腺癌的生存率由于其筛查和诊断方法的不断增加而上升。但是，乳腺癌仍然是女性发现的最具侵入性的疾病。近年来，使用成像方式研究乳腺癌的许多技术正在兴起。本文打算使用改进的crow-search最优化算法（ImCSOA）将数字化乳腺X线照片中出现的严重性分类为良性（B）或恶性（M）。在文献中，CSOA通常用于解决几个特征选择和数值优化问题。目的是利用这种流行的优化算法解决生物医学图像分类问题。但是，如果将此算法直接应用于分类问题，则将导致数据分类不佳。因此，原始的CSO（OCSO）算法通过使用新颖的受控参数调整，控制算子和基于混沌映射的受控随机性进行了适当的增强。四个不同的混沌图用于控制OCSO算法中的随机性。乳房X射线照片图像从乳房X射线图像分析学会和数字数据库获得，用于筛选乳房X射线照片数据集以进行评估。通过基于离散小波变换的统计特征完成分类，该特征在分解的两个级别（级别4（L4）和级别6（L6））处提取。对于这两个数据集，具有L4和L6分解的bior4.4小波特征的ImCSOA提供的最大准确度约为85％至86％，比具有L4和L6分解的bior4.4的OCSO算法约高62％至88％小波特征。原始的CSO（OCSO）算法通过使用新颖的受控参数调整，控制算子和基于混沌映射的受控随机性进行了适当的增强。四个不同的混沌图用于控制OCSO算法中的随机性。乳房X射线照片图像从乳房X射线图像分析学会和数字数据库获得，用于筛选乳房X射线照片数据集以进行评估。通过基于离散小波变换的统计特征完成分类，该特征在分解的两个级别（级别4（L4）和级别6（L6））处提取。对于这两个数据集，具有L4和L6分解的bior4.4小波特征的ImCSOA提供的最大准确度约为85％至86％，比具有L4和L6分解的bior4.4的OCSO算法约高62％至88％小波特征。原始的CSO（OCSO）算法通过使用新颖的受控参数调整，控制算子和基于混沌映射的受控随机性进行了适当的增强。四个不同的混沌图用于控制OCSO算法中的随机性。乳房X射线照片图像从乳房X射线图像分析学会和数字数据库获得，用于筛选乳房X射线照片数据集以进行评估。通过基于离散小波变换的统计特征完成分类，该特征在分解的两个级别（级别4（L4）和级别6（L6））处提取。对于这两个数据集，具有L4和L6分解的bior4.4小波特征的ImCSOA提供的最大准确度约为85％至86％，比具有L4和L6分解的bior4.4的OCSO算法约高62％至88％小波特征。