COVID-19 is a deadly viral infection that has brought a significant threat to human lives. Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and reinforces coronavirus testing methods in place. While there exist several challenges in manually inferring traces of this viral infection from X-ray, Convolutional Neural Network (CNN) can mine data patterns that capture subtle distinctions between infected and normal X-rays. To enable automated learning of such latent features, a custom CNN architecture has been proposed in this research. It learns unique convolutional filter patterns for each kind of pneumonia. This is achieved by restricting certain filters in a convolutional layer to maximally respond only to a particular class of pneumonia/COVID-19. The CNN architecture integrates different convolution types to aid better context for learning robust features and strengthen gradient flow between layers. The proposed work also visualizes regions of saliency on the X-ray that have had the most influence on CNN’s prediction outcome. To the best of our knowledge, this is the first attempt in deep learning to learn custom filters within a single convolutional layer for identifying specific pneumonia classes. Experimental results demonstrate that the proposed work has significant potential in augmenting current testing methods for COVID-19. It achieves an F1-score of 97.20% and an accuracy of 99.80% on the COVID-19 X-ray set.

COVID-19 是一种致命的病毒感染，给人类生命带来了重大威胁。通过医学影像自动诊断 COVID-19 可以实现精准用药，有助于控制社区疫情，并加强现有的冠状病毒检测方法。虽然从 X 射线手动推断这种病毒感染的痕迹存在一些挑战，但卷积神经网络 (CNN) 可以挖掘数据模式，捕获感染 X 射线和正常 X 射线之间的细微区别。为了实现此类潜在特征的自动学习，本研究提出了一种定制的 CNN 架构。它学习每种肺炎的独特卷积过滤模式。这是通过限制卷积层中的某些过滤器最大程度地仅响应特定类别的肺炎/COVID-19 来实现的。 CNN 架构集成了不同的卷积类型，以帮助更好的上下文来学习稳健的特征并加强层之间的梯度流。这项工作还可视化了 X 射线上对 CNN 预测结果影响最大的显着区域。据我们所知，这是深度学习中首次尝试在单个卷积层中学习自定义过滤器来识别特定的肺炎类别。实验结果表明，所提出的工作在增强当前的 COVID-19 测试方法方面具有巨大潜力。它在 COVID-19 X 射线装置上的 F1 分数为 97.20%，准确度为 99.80%。