Cough Sounds, Symptoms, CXR, and CT Images for COVID-19 Detection

Since the coronavirus epidemic has reached every corner of the globe, we need to develop machine-based solutions to detect it. The global spread of novel coronaviruses has claimed millions of lives and prompted the development of various low-cost diagnostic assays for the presence of COVID-19 infection. The identification of chronic and communicable diseases is only one example of how current technology and transfer learning (TL) methodologies have improved human health and wellness. In order to combat the spread of this terrible infection, intelligence-based models and preventive measures and much research are required. This review presents a comprehensive analysis of the literature on TL and fuzzy ensemble techniques related to COVID-19 detection procedures. For the purpose of identifying COVID-19, researchers have used cough sounds, CT scans, X-ray pictures, and a symptom dataset. In this study, we explore the use of DL/ML, TL, fuzzy ensemble, and fuzzy inference methods for identifying COVID-19.