Non-Invasive COVID-19 Screening Using Deep Learning-Based Multilevel Fusion Model With an Attention Mechanism

The current pandemic has necessitated rapid and automatic detection of coronavirus disease (COVID-19) infections. Various AI functionalities coupled with biomedical images can be utilized to efficiently detect these infections and recommend a prompt response (curative intervention) to limit the virus's spread. In particular, biomedical imaging could help to visualize the internal organs of the human body and disorders that affect them. One of them is chest X-rays (CXR) which has widely been used for preventive medicine or disease screening. However, when it comes to detecting COVID-19 from CXR images, most of the approaches rely on standard image classification algorithms, which have limitations with low identification accuracy and improper extraction of key features. As a result, a CNN-based fusion network has been developed for automated COVID-19 screening in this study. First, using attention networks and multiple fine-tuned CNN models, we extract key features that are resistant to overfitting. We then employ a locally connected layer to create a weighted combination of these models for final COVID-19 detection. Using a publicly available dataset of CXR images from healthy subjects as well as COVID-19 and pneumonia cases, we evaluated the predictive capabilities of our proposed model. Test results demonstrate that the proposed fusion model performs favorably compared to individual CNN models.