Coronavirus (COVID-19) and its new strain resulted in massive damage to society and brought panic worldwide. Automated medical image analysis such as X-rays, CT, and MRI offers excellent early diagnosis potential to augment the traditional healthcare strategy to fight against COVID-19. However, the identification of COVID infected lungs X-rays is challenging due to the high variation in infection characteristics and low-intensity contrast between normal tissues and infections. To identify the infected area, in this work, we present a novel depth-wise dense network that uniformly scales all dimensions and performs multilevel feature embedding, resulting in increased feature representations. The inclusion of depth wise component and squeeze-and-excitation results in better performance by capturing a more receptive field than the traditional convolutional layer; however, the parameters are almost the same. To improve the performance and training set, we have combined three large scale datasets. The extensive experiments on the benchmark X-rays datasets demonstrate the effectiveness of the proposed framework by achieving 96.17% in comparison to cutting-edge methods primarily based on transfer learning.

冠状病毒（COVID-19）及其新毒株对社会造成了巨大破坏，并在全球范围内引起了恐慌。X 射线、CT 和 MRI 等自动化医学图像分析提供了出色的早期诊断潜力，可增强传统医疗保健策略以对抗 COVID-19。然而，由于感染特征的高度变化以及正常组织和感染之间的低强度对比，识别 COVID 感染的肺部 X 射线具有挑战性。为了识别感染区域，在这项工作中，我们提出了一种新颖的深度密集网络，它统一缩放所有维度并执行多级特征嵌入，从而增加了特征表示。与传统卷积层相比，深度分量和挤压和激发的包含通过捕获更多的感受野来获得更好的性能；但是，参数几乎相同。为了提高性能和训练集，我们结合了三个大型数据集。与主要基于迁移学习的前沿方法相比，基准 X 射线数据集上的广泛实验证明了所提出框架的有效性，达到了 96.17%。