Das Sreeparna, Ayus Ishan, Gupta Deepak
Department of Computer Science and Engineering, National Institute of Technology Arunachal Pradesh, Jote, Arunachal Pradesh 791113 India.
Department of Computer Science and Engineering, ITER, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha 751030 India.
Health Technol (Berl). 2023 Jun 7:1-14. doi: 10.1007/s12553-023-00757-z.
The first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement.
The researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected.
In those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research.
In conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.
冠状病毒首次传播至人类是在中国武汉市开始的，演变成了一场名为2019冠状病毒病（COVID-19）的大流行，并对整个世界构成了主要威胁。研究人员正试图引入人工智能（机器学习或深度学习模型）以实现对COVID-19的高效检测。本研究探讨了所有用于COVID-19检测的现有机器学习（ML）或深度学习（DL）模型，这可能有助于研究人员在不同方向上进行探索。这篇综述文章的主要目的是向研究专家简要介绍人工智能的应用，帮助他们探索未来改进的方向。
研究人员使用了各种机器学习、深度学习以及机器学习与深度学习相结合的模型来提取显著特征并对COVID-19患者的各种健康状况进行分类。为此，研究人员利用了不同的图像模态，如CT扫描、X射线等。本研究从谷歌学术、PubMed、科学网等各种数据库中收集了200多篇研究论文。这些研究论文经过了多轮严格筛选，最终选出了50篇研究文章。
在那些列出的文章中，ML/DL模型在对COVID-19进行分类时显示出99%及以上的准确率。本研究还展示了各项研究的不同临床应用。这项研究明确了各种机器学习和深度学习模型在医学诊断和研究领域的重要性。
总之，很明显ML/DL模型近年来取得了显著进展，但仍存在需要解决的局限性。过拟合就是这样一种局限性，它可能导致错误的预测以及模型的负担过重。研究界必须继续努力寻找克服这些局限性的方法，使机器学习和深度学习模型更加有效和高效。通过持续的研究与开发，我们有望在未来取得更大的进展。
