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Deep reinforcement learning with its application for lung cancer detection in medical Internet of Things

机译：深度加强学习，其应用于医学互联网肺癌检测

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Recently, deep reinforcement learning has achieved great success by integrating deep learning models into reinforcement learning algorithms in various applications such as computer games and robots. Specially, it is promising for computer-aided diagnosis and treatment to combine deep reinforcement learning with medical big data generated and collected from medical Internet of Things. In this paper, we focus on the potential of the deep reinforcement learning for lung cancer detection as many people are suffering from the lung tumor and about 1.8 million patients died from lung cancer in 2018. Early detection and diagnosis of lung tumor can significantly improve the treatment effect and prolong survival. In this work, we present several representative deep reinforcement learning models that are potential to use for lung cancer detection. Furthermore, we summarize the common types of lung cancer and the main characteristics of each type. Finally, we point out the open challenges and possible future research directions of applying deep reinforcement learning to lung cancer detection, which is expected to promote the evolution of smart medicine with medical Internet of Things. (C) 2019 Elsevier B.V. All rights reserved.
机译：最近，深入的加强学习通过将深度学习模型集成到计算机游戏和机器人等各种应用中的加固学习算法中取得了巨大的成功。特别是，对计算机辅助诊断和治疗有前途，将深度加强学习与生成的医疗大数据相结合，并从医疗互联网上收集。在本文中，我们专注于对肺癌检测的深度增强学习的潜力，因为许多人患有肺癌和约180万患者在2018年死于肺癌。肺肿瘤的早期检测和诊断可以显着改善治疗效果和延长存活。在这项工作中，我们提出了几种具有用于肺癌检测的可能性的代表性深增强学习模型。此外，我们总结了肺癌的常见类型和每种类型的主要特征。最后，我们指出了对肺癌检测应用深度增强学习的未来挑战和可能的未来研究方向，预计将促进智能医学与医疗器材的演变。 （c）2019 Elsevier B.v.保留所有权利。

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