Cenggoro Tjeng Wawan, Pardamean Bens
Computer Science Department, BINUS Graduate Program - Master of Computer Science Program, Bina Nusantara University, Jakarta 11480, Indonesia.
Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia.
Procedia Comput Sci. 2023;216:749-756. doi: 10.1016/j.procs.2022.12.192. Epub 2023 Jan 10.
Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in this sector. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. According to the findings, chest X-ray were the most commonly used data to categorize COVID-19 and transfer learning techniques were the method used in this study. Researchers also concluded that lung segmentation and use of multimodal data could improve performance.
尽早且快速地检测出新冠病毒是阻止新冠病毒传播的一种方式。机器学习的发展有助于更快、更准确地诊断新冠病毒。本报告旨在了解该领域的研究进展程度以及未来研究可以吸取哪些经验教训。通过在谷歌学术数据库中筛选标题、摘要和内容，本次文献综述找到了19篇相关论文，以回答两个研究问题，即新冠病毒分类通常使用哪些医学图像以及新冠病毒分类的方法有哪些。根据研究结果，胸部X光片是用于新冠病毒分类最常用的数据，迁移学习技术是本研究中使用的方法。研究人员还得出结论，肺部分割和多模态数据的使用可以提高性能。
