Deep-reinforcement-learning-based images segmentation for quantitative analysis of gold immunochromatographic strip

Abstract Gold immunochromatographic strip (GICS) is a widely used lateral flow immunoassay technique. A novel image segmentation method is developed in this paper for quantitative analysis of GICS based on the deep reinforcement learning (DRL), which can accurately distinguish the test line and the control line in the GICS images. The deep belief network (DBN) is employed in the deep Q network in our DRL algorithm. Meanwhile, the multi-factor learning curve is introduced in the DRL algorithm to dynamically adjust the capacity of the replay buffer and the sampling size, which leads to enhanced learning efficiency. It is worth mentioning that the states, actions, and rewards in the developed DRL algorithm are determined based on the characteristics of GICS images. Experiment results demonstrate the feasibility and reliability of the proposed DRL-based image segmentation method and show that the proposed new image segmentation method outperforms some existing image segmentation methods for quantitative analysis of GICS images.