Bhargava Anuja, Bansal Atul
GLA University, Mathura, India.
Multimed Tools Appl. 2021;80(13):19931-19946. doi: 10.1007/s11042-021-10714-5. Epub 2021 Mar 3.
The universal transmission of pandemic COVID-19 (Coronavirus) causes an immediate need to commit in the fight across the whole human population. The emergencies for human health care are limited for this abrupt outbreak and abandoned environment. In this situation, inventive automation like computer vision (machine learning, deep learning, artificial intelligence), medical imaging (computed tomography, X-Ray) has developed an encouraging solution against COVID-19. In recent months, different techniques using image processing are done by various researchers. In this paper, a major review on image acquisition, segmentation, diagnosis, avoidance, and management are presented. An analytical comparison of the various proposed algorithm by researchers for coronavirus has been carried out. Also, challenges and motivation for research in the future to deal with coronavirus are indicated. The clinical impact and use of computer vision and deep learning were discussed and we hope that dermatologists may have better understanding of these areas from the study.
新冠大流行（冠状病毒）的广泛传播使得立即需要全人类共同投身抗疫斗争。由于此次疫情突然爆发以及环境因素，人类医疗保健面临的紧急情况十分有限。在这种情况下，诸如计算机视觉（机器学习、深度学习、人工智能）、医学成像（计算机断层扫描、X射线）等创新性自动化技术为抗击新冠疫情提供了令人鼓舞的解决方案。近几个月来，不同的研究人员采用了各种图像处理技术。本文对图像采集、分割、诊断、预防及管理进行了全面综述。对研究人员针对冠状病毒提出的各种算法进行了分析比较。此外，还指出了未来应对冠状病毒研究的挑战与动力。文中讨论了计算机视觉和深度学习的临床影响及应用，希望皮肤科医生能通过本研究更好地了解这些领域。
