MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis
This work addresses the need for efficient COVID-19 screening to optimize healthcare resources and reduce clinician workload, but it appears incremental as it builds on existing transfer learning methods.
The authors tackled the problem of rapid COVID-19 diagnosis using CT images by proposing the MEDNC deep learning framework, which achieved accuracies of 98.79% and 99.82% on COVID-19 datasets and also performed well on brain tumor and blood cell datasets with accuracies of 99.39% and 99.28%.
Coronavirus disease 2019 (COVID-19) has spread all over the world for three years, but medical facilities in many areas still aren't adequate. There is a need for rapid COVID-19 diagnosis to identify high-risk patients and maximize the use of limited medical resources. Motivated by this fact, we proposed the deep learning framework MEDNC for automatic prediction and diagnosis of COVID-19 using computed tomography (CT) images. Our model was trained using two publicly available sets of COVID-19 data. And it was built with the inspiration of transfer learning. Results indicated that the MEDNC greatly enhanced the detection of COVID-19 infections, reaching an accuracy of 98.79% and 99.82% respectively. We tested MEDNC on a brain tumor and a blood cell dataset to show that our model applies to a wide range of problems. The outcomes demonstrated that our proposed models attained an accuracy of 99.39% and 99.28%, respectively. This COVID-19 recognition tool could help optimize healthcare resources and reduce clinicians' workload when screening for the virus.