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Prediction of Lung Cancer using Meta-Heuristic based Optimization Technique: Crow Search Technique

机译:基于元启发式优化技术的肺癌预测:乌鸦搜索技术

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In this paper, a meta-heuristic-based optimization technique is used for predicting cancer in the lungs. Lung cancer is one of the dreadful diseases that occurred in human beings and cause a lot of damage. Even though, lots of techniques have been proposed to predict lung cancer. But, still, the prediction of cancer has become a challenging task, due to the multifaceted structures in the CT scan. The automated meta-heuristic-based optimization technique namely crow search algorithm is used to find the feasible position and then searching the similar pixels for the clustering process. The exact position of the tumor is finding out using the crow search algorithm and can be better visualized. This process helps the radiologist to find the tumor in the beginning stage and acts as an efficient method for the prediction process. The sensitivity values (99.12 %) produced by the crow search technique is promising than the conventional techniques.
机译:本文使用了荟萃启发式的优化技术用于预测肺部癌症。肺癌是人类发生的可怕疾病之一,并导致大量损害。尽管如此,已经提出了许多技术来预测肺癌。但是,由于CT扫描中的多方面结构,仍然存在对癌症的预测已成为一个具有挑战性的任务。基于自动化的Meta-heuristic的优化技术即乌鸦搜索算法用于找到可行的位置,然后搜索群集过程的类似像素。肿瘤的确切位置使用乌鸦搜索算法来发现,可以更好地可视化。该过程有助于放射学家在开始阶段找到肿瘤,并充当预测过程的有效方法。由乌鸦搜索技术产生的灵敏度值(99.12%)比传统技术在一起。

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