An Efficient Multilevel Threshold Image Segmentation Method for COVID-19 Imaging Using Q-Learning Based Golden Jackal Optimization

From the end of 2019 until now, the Coronavirus Disease 2019 (COVID-19) has been rampaging around the world, posing a great threat to people's lives and health, as well as a serious impact on economic development. Considering the severely infectious nature of COVID-19, the diagnosis of COVID-19 has become crucial. Identification through the use of Computed Tomography (CT) images is an efficient and quick means. Therefore, scientific researchers have proposed numerous segmentation methods to improve the diagnosis of CT images. In this paper, we propose a reinforcement learning-based golden jackal optimization algorithm, which is named QLGJO, to segment CT images in furtherance of the diagnosis of COVID-19. Reinforcement learning is combined for the first time with meta-heuristics in segmentation problem. This strategy can effectively overcome the disadvantage that the original algorithm tends to fall into local optimum. In addition, one hybrid model and three different mutation strategies were applied to the update part of the algorithm in order to enrich the diversity of the population. Two experiments were carried out to test the performance of the proposed algorithm. First, compare QLGJO with other advanced meta-heuristics using the IEEE CEC2022 benchmark functions. Secondly, QLGJO was experimentally evaluated on CT images of COVID-19 using the Otsu method and compared with several well-known meta-heuristics. It is shown that QLGJO is very competitive in benchmark function and image segmentation experiments compared with other advanced meta-heuristics. Furthermore, the source code of the QLGJO is publicly available at https://github.com/Vang-z/QLGJO.