使用元启发式和深度学习的CT图像的癌症诊断。

IF 1.5 4区 医学 Q3 ENGINEERING, BIOMEDICAL Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine Pub Date : 2024-04-01 Epub Date: 2022-04-21 DOI:10.1177/09544119221090725
Qiufang Ma, Giorgos Jimenez
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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。
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RETRACTED: Lung cancer diagnosis of CT images using metaheuristics and deep learning.
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来源期刊
CiteScore
3.60
自引率
5.60%
发文量
122
审稿时长
6 months
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.
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