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首页> 外文期刊>International journal of imaging systems and technology >A novel improved crow-search algorithm to classify the severity in digital mammograms
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A novel improved crow-search algorithm to classify the severity in digital mammograms

机译：一种新颖的改进乌鸦搜索算法，用于对数字乳房X光检查的严重性进行分类

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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.
机译：由于其筛查和诊断方法的出现增加，乳腺癌的存活率正在上升。然而，乳腺癌尚未成为女性中最具侵入性的疾病。在近年来使用成像方式调查乳腺癌的近年来正在出现许多技术。本文旨在使用改进的乌鸦搜索优化算法（IMCSOA）将数字乳房图像图像中存在的严重程度分类为良性（B）或恶性物质（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来解决生物医学图像分类问题。但是，如果此算法直接应用于分类问题，那么它将导致数据分类差。因此，原始CSO（OCSO）算法使用新颖的受控参数调谐，控制操作员和基于混沌映射的受控随机性进行合适的增强。四个不同的混沌映射用于控制OCSO算法中的随机性。乳房X线图图像是从乳房X线图图像分析社会和数字数据库获得的，用于筛选用于评估的乳房X线摄影数据集。通过在分解的两个级别[级别4（L4）和级别6（L6）]中提取的基于基于小波变换的统计特征来实现分类。对于两个数据集，具有L4和L6的IMCSOA分解的BIOR4.4小波特征提供了大约85％至86％的最大精度，而不是L4和L6分解Bior4.4的OCSO算法大约62％至88％。小波特征。

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