ABSTRACT This work, primarily, addresses the problem of automated diagnosis of lung cancer by classifying malignant nodules present in the lung, if any. To achieve the goal, we have posed a weighted dual objective optimisation problem so as to reduce the feature subset required for automated classification of malignant lung nodules and at the same time, we endeavour to increase the classification accuracy to minimise the false impression. The values of the respective weights associated with the discriminating feature subset and the classification accuracy have been evaluated in such a way that the accuracy of nodule classification increases with the help of a small discriminating feature subset. The strategic weight selection methodology, presented in this work, helps attain an optimal combination of these two objectives. As a solution methodology, subsequently, we propose a new adaptive weighted aggregation strategy based on an evolutionary optimisation technique, popularly known as Group Improvised Harmony Search (GrIHS). An adaptive KNN classifier has also been embedded with the GrIHS method to determine the optimal number of neighbourhoods, in the search space, that would further aid in increasing the nodule classification accuracy. The proposed method has been successfully applied to classify the lung nodules from the Lung Image Database Consortium (LIDC) database. The experimental results demonstrate a noteworthy efficacy of the proposition with sensitivity of 97.59% and 97.78% blind testing accuracy using only 12 discriminating features. Hence, the proposed methodology can be used to support the diagnosis pronounced by the radiologists interpreting manually the lung computed tomography images.

中文翻译：

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Group Improvised Harmony Search中的自适应加权聚合用于肺结节分类

摘要这项工作主要通过对肺部存在的恶性结节（如果有）进行分类来解决肺癌的自动诊断问题。为了实现这一目标，我们提出了一个加权双目标优化问题，以减少恶性肺结节自动分类所需的特征子集，同时，我们努力提高分类精度，以最大限度地减少错误印象。与判别特征子集和分类准确度相关联的各个权重的值已经以这样的方式进行评估，即在小的判别特征子集的帮助下，结节分类的准确度增加。这项工作中介绍的战略权重选择方法有助于实现这两个目标的最佳组合。作为一种解决方法，随后，我们提出了一种基于进化优化技术的新的自适应加权聚合策略，俗称组即兴和谐搜索（GrIHS）。GrIHS 方法还嵌入了自适应 KNN 分类器，以确定搜索空间中的最佳邻域数，这将进一步有助于提高结节分类的准确性。所提出的方法已成功应用于肺图像数据库联盟 (LIDC) 数据库中的肺结节分类。实验结果证明了该命题的显着功效，其灵敏度为 97.59%，盲测准确度为 97.78%，仅使用 12 个判别特征。因此，