COVID-19 with H1N1 dataset

COVID-19 and swine-origin influenza A (H1N1) are both pandemics that sparked significant concern worldwide. Since these two diseases have common symptoms, a fast COVID-19 versus H1N1 screening helps better manage patients at healthcare facilities. We present a novel deep model, called Optimized Parallel Inception, for fast screening of COVID-19 and H1N1 patients. We also present a Semi-supervised Generative Adversarial Network (SGAN) to address the problem related to the smaller size of the COVID-19 and H1N1 research data. To evaluate the proposed models, we have merged two separate COVID-19 and H1N1 data from different sources to build a new dataset. The created dataset includes 4,383 positive COVID-19 cases, 989 positive H1N1 cases, and 1,059 negative cases. We applied SGAN on this dataset to remove issues related to unequal class densities. The experimental results show that the proposed model's screening accuracy is 99.2\% and 99.6\% for COVID-19 and H1N1, respectively. According to our analysis, the most significant symptoms and underlying chronic diseases for COVID-19 versus H1N1 screening are dry cough, breathing problems, diabetes, and gastrointestinal.

COVID-19与猪源性甲型流感（H1N1）均为曾引发全球广泛关注的大流行病。由于二者临床症状存在诸多共性，快速区分COVID-19与H1N1的筛查工作可助力医疗机构更高效地开展患者管理。本研究提出一种名为优化并行Inception（Optimized Parallel Inception）的新型深度学习模型，用于快速筛查COVID-19与H1N1感染患者。针对COVID-19与H1N1研究数据规模偏小的问题，我们同时提出了半监督生成对抗网络（Semi-supervised Generative Adversarial Network，SGAN）以解决该类数据不足带来的痛点。为评估所提出的模型，我们整合了来自不同数据源的两组独立COVID-19与H1N1数据集，构建了全新的研究数据集。该数据集包含4383例COVID-19阳性病例、989例H1N1阳性病例以及1059例阴性病例。我们在该数据集上应用SGAN以缓解类别分布不均衡的问题。实验结果表明，所提模型对COVID-19与H1N1的筛查准确率分别可达99.2%与99.6%。经分析，用于区分COVID-19与H1N1的最显著筛查相关症状及基础性慢性病为干咳、呼吸障碍、糖尿病及胃肠道相关病症。