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

Abstract 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.