Improving COVID-19 Detection Through Cooperative Deep-Learning Pipeline for Lung Semantic Segmentation in Medical Imaging

The global impact of COVID-19 has resulted in millions of individuals being afflicted, with a staggering mortality toll of over 16 000 over a span of 2 years. The dearth of resources and diagnostic techniques has had an impact on both emerging and wealthy nations. In response to this, researchers from the domains of engineering and medicine are using deep learning methods to create automated algorithms for detecting COVID-19. This work included the development and comparison of a collaborative deep-learning model for the identification of COVID-19 using CT scan images, in comparison to previous deep learning-based methods. The model underwent an ablation study using publicly accessible COVID-19 CT imaging datasets, with encouraging outcomes. The suggested model might aid doctors and academics in devising tools to expedite the process of determining the optimal therapeutic approach for health professionals, hence reducing the risk of potential problems.