The gold standard for COVID-19 is RT-PCR, testing facilities for which are limited and not always optimally distributed. Test results are delayed, which impacts treatment. Expert radiologists, one of whom is a co-author, are able to diagnose COVID-19 positivity from Chest X-Rays (CXR) and CT scans, that can facilitate timely treatment. Such diagnosis is particularly valuable in locations lacking radiologists with sufficient expertise and familiarity with COVID-19 patients. This paper has two contributions. One, we analyse literature on CXR based COVID-19 diagnosis. We show that popular choices of dataset selection suffer from data homogeneity, leading to misleading results. We compile and analyse a viable benchmark dataset from multiple existing heterogeneous sources. Such a benchmark is important for realistically testing models. Our second contribution relates to learning from imbalanced data. Datasets for COVID X-Ray classification face severe class imbalance, since most subjects are COVID -ve. Twin Support Vector Machines (Twin SVM) and Twin Neural Networks (Twin NN) have, in recent years, emerged as effective ways of handling skewed data. We introduce a state-of-the-art technique, termed as Twin Augmentation, for modifying popular pre-trained deep learning models. Twin Augmentation boosts the performance of a pre-trained deep neural network without requiring re-training. Experiments show, that across a multitude of classifiers, Twin Augmentation is very effective in boosting the performance of given pre-trained model for classification in imbalanced settings.
翻译:COVID-19 的黄金标准是 RT-PCR, 测试设施有限, 且不总是最佳分布。 测试结果被推迟, 影响治疗。 专家放射学家(其中一位是共同作者)能够从Chest X- Rays (CXR) 和 CT 扫描中诊断出COVID-19 的真能性, 这可以促进及时治疗。 这种诊断在缺乏拥有足够专业知识和熟悉COVID-19 病人的放射学家的地点特别宝贵。 本文有两点贡献。 第一, 我们分析基于 CXR COVID-19 诊断的文献。 我们显示, 普通选择数据集选择会受到数据正值的同一性能影响, 导致误导结果。 我们汇编和分析了来自多种现有多样性来源的可行基准数据集。 这种基准对于现实测试模型很重要。 我们的第二个贡献是学习不平衡的数据。 COVID X- Ray分类的数据集面临严重的阶级模型, 因为大多数是 COVID-ve。 双向支持深度分类机(Twin SVM) 和双神经网络(Twin Necialweal netweal ) 网络(Twin Negration) rodustration) 正在开始一种有效的升级的运行, 。