Fast COVID-19 versus H1N1 screening using Optimized Parallel Inception. (15th October 2022) - Tavakolian, Alireza; Hajati, Farshid; Rezaee, Alireza; Fasakhodi, Amirhossein Oliaei; Uddin, Shahadat
Abstract: 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. Highlights: The first model to fast screen patients of coincident COVID-19 and H1N1 pandemics. A novel Optimized Parallel Inception model to fast screen COVID-19 and H1N1 patients. A Semi-supervised Generative Adversarial Network to address the smaller size of data. The most significant features in COVID-19 versus H1N1 screening are identified. The model helps the healthcare providers in pandemics by rapid screening.