基于深度学习的肺癌计算机断层扫描分类方法综述

Mario G. Borja Borja, Roger Huauya, Cristian Lazo
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引用次数: 0

摘要

在本文中,我们对深度学习方法在解决使用计算机断层扫描检测肺癌的重要任务方面进行了简要但批判性的调查。这是一篇调查论文,旨在为读者提供解决这一任务的前沿算法。我们回顾了与此主题相关的20多篇论文,以涵盖解决此问题的最佳方法。此外,我们的工作不仅在算法方面进行了回顾,而且在输入数据集,计算机断层扫描方面也进行了回顾。最后,我们总结了目前最先进的方法,对改进的算法进行了总体分析,并提出了解决计算机断层扫描中肺癌分类任务的一些注意事项。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A brief survey on deep learning based methods for lung cancer classification using computerized tomography scans
In this paper, we present a brief but critic survey of deep learning approaches in solving the remarkable task of lung cancer detection using computerized tomography scans. This is a survey paper that is intended to give the reader the cuttingedge algorithms to solve this task. We reviewed over 20 papers related to this topic to cover the best methods to approach this problem. In addition, our work develops a review not only in the algorithm, but also in the input dataset, the computerized tomography scans. At the end, we conclude with a summary of the current state-of-the-art methods, an overall analysis of the algorithms revised and some considerations to solve the lung cancer classification task in computerized tomography.
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