Infectious diseases are highly contagious due to rapid transmission and very challenging to diagnose in the early stage. Artificial Intelligence and Machine Learning now become a strategic weapon in assisting infectious disease prevention, rapid-response in diagnosis, surveillance, and management. In this paper, a bifold COVID_SCREENET architecture is introduced for providing COVID-19 screening solutions using Chest Radiography (CR) images. Transfer learning using nine pre-trained ImageNet models to extract the features of Normal, Pneumonia, and COVID-19 images is adapted in the first fold and classified using baseline Convolutional Neural Network (CNN). A Modified Stacked Ensemble Learning (MSEL) is proposed in the second fold by stacking the top five pre-trained models, and then the predictions resulted. Experimentation is carried out in two folds: In first fold, open-source samples are considered and in second fold 2216 real-time samples collected from Tamilnadu Government Hospitals, India, and the screening results for COVID data is 100% accurate in both the cases. The proposed approach is also validated and blind reviewed with the help of two radiologists at Thanjavur Medical College & Hospitals by collecting 2216 chest X-ray images between the month of April and May. Based on the reports, the measures are calculated for COVID_SCREENET and it showed 100% accuracy in performing multi-class classification.

传染病由于传播速度快，具有高度传染性，早期诊断非常具有挑战性。人工智能和机器学习现已成为协助传染病预防、诊断、监测和管理快速响应的战略武器。在本文中，介绍了一种双折 COVID_SCREENET 架构，用于使用胸片 (CR) 图像提供 COVID-19 筛查解决方案。使用九个预训练的 ImageNet 模型提取正常、肺炎和 COVID-19 图像的特征的迁移学习在第一折中进行调整，并使用基线卷积神经网络 (CNN) 进行分类。通过堆叠前五个预训练模型，在第二折中提出了改进的堆叠集成学习（MSEL），然后得到预测。实验分两部分进行：第一部分考虑开源样本，第二部分是从印度泰米尔纳德邦政府医院收集的 2216 个实时样本，两种情况下 COVID 数据的筛选结果都是 100% 准确的. 在坦贾武尔医学院和医院的两名放射科医生的帮助下，通过在 4 月至 5 月期间收集 2216 张胸部 X 光图像，该提议的方法也得到了验证和盲审。根据报告，针对 COVID_SCREENET 计算了这些措施，并且在执行多类分类时显示了 100% 的准确度。在坦贾武尔医学院和医院的两名放射科医生的帮助下，通过在 4 月至 5 月期间收集 2216 张胸部 X 光图像，该提议的方法也得到了验证和盲审。根据报告，针对 COVID_SCREENET 计算了这些措施，并且在执行多类分类时显示了 100% 的准确度。在坦贾武尔医学院和医院的两名放射科医生的帮助下，通过在 4 月至 5 月期间收集 2216 张胸部 X 光图像，该提议的方法也得到了验证和盲审。根据报告，针对 COVID_SCREENET 计算了这些措施，并且在执行多类分类时显示了 100% 的准确度。