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引用次数: 0
摘要
癌症是由胸部的两个海绵状器官组成的肺部细胞不受控制的生长。这些细胞可能在一个称为转移的过程中渗透到肺外,并扩散到身体的组织和器官,增加死于这种疾病的风险。肺结节CT扫描是癌症早期诊断的方法之一。诊断肺结节的主要挑战之一是难以识别和区分肺结节和肺成分。在这项研究中,引入了一个计算机辅助检测系统来识别这些结节。在本研究中,经过图像预处理,提出了一种基于Otsu和数学形态学的图像分割方法。然后,基于一种新的元启发式方法来选择最优特征。因此,将这些特征注入到改进的基于卷积神经网络(CNN)的分类器中,以提供高精度的诊断系统。Otsu方法、特征选择和CNN分类器的优化是通过Red Fox Optimizer(RFO)算法的新修改版本建立的。然后将该方法应用于三个流行的癌症数据集,并将结果与三种最先进的方法进行比较,以显示所提出的方法的更高效率。
期刊介绍:
The Journal of Engineering in Medicine is an interdisciplinary journal encompassing all aspects of engineering in medicine. The Journal is a vital tool for maintaining an understanding of the newest techniques and research in medical engineering.