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

作     者：Zihao Wang Yuanbin Mo Mingyue Cui Zihao Wang;Yuanbin Mo;Mingyue Cui

作者机构：School of Artificial IntelligenceGuangxi Minzu UniversityNanningChina Guangxi Key Laboratory of Hybrid Computation and IC Design AnalysisGuangxi Minzu UniversityNanning530006China 

出 版 物：《Journal of Bionic Engineering》 (仿生工程学报（英文版）)

年 卷 期：2023年第20卷第5期

页      面：2276-2316页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 081203[工学-计算机应用技术] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China[grant numbers 21466008] the Guangxi Natural Science Foundation,China[grant numbers 2019GXNSFAA185017] the Scientific Research Project of Guangxi Minzu University[grant numbers 2021MDKJ004] the Innovation Project of Guangxi Graduate Education[grant numbers YCSW2022255]. 

主　　题：COVID-19 Bionic algorithm Golden jackal optimization Image segmentation Otsu and Kapur method 

摘      要：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://***/Vang-z/QLGJO.