Dubey Ankit Kumar, Mohbey Krishna Kumar
Department of Computer Science, Central University of Rajasthan, Ajmer, India.
J Biomol Struct Dyn. 2023 Apr;41(6):2528-2539. doi: 10.1080/07391102.2022.2034668. Epub 2022 Feb 6.
Today, we are coping with the pandemic, and the novel virus is covertly evolving day by day. Therefore, a precautionary system to deal with the issue is required as early as possible. The last few years were very challenging for doctors, vaccine makers, hospitals, and medical authorities to deal with the massive crowd to provide results for all patients and newcomers in the past months. Thus, these issues should be handled with a robust system that can accord with many people and deliver the results in a fraction of time without visiting public places and help reduce crowd gathering. So, to deal with these issues, we developed an AI model using transfer learning that can aid doctors and other people to get to know whether they were suffering from covid or not. In this paper, we have used VGG-19 (CNN-based) model with open-sourced COVID-CT (CTSI) dataset. The dataset consists of 349 images of COVID-19 of 216 patients and 463 images of NON-COVID-19. We have achieved an accuracy of 95%, precision of 96%, recall of 94%, and F1-Score of 96% from the experiments.Communicated by Ramaswamy H. Sarma.
如今，我们正在应对疫情，这种新型病毒每天都在悄然演变。因此，需要尽早建立一个应对该问题的预防系统。在过去几个月里，医生、疫苗制造商、医院和医疗机构面临着巨大挑战，要为所有患者和新患者提供检测结果。因此，这些问题应该通过一个强大的系统来处理，该系统能够满足许多人的需求，并在短时间内得出结果，无需前往公共场所，有助于减少人群聚集。所以，为了应对这些问题，我们利用迁移学习开发了一个人工智能模型，该模型可以帮助医生和其他人了解自己是否感染了新冠病毒。在本文中，我们使用了基于卷积神经网络（CNN）的VGG - 19模型和开源的COVID - CT（CTSI）数据集。该数据集包含216名患者的349张新冠病毒图像和463张非新冠病毒图像。通过实验，我们实现了95%的准确率、96%的精确率、94%的召回率和96%的F1分数。由拉马斯瓦米·H·萨尔马传达。
