An Advanced COVID-19 Severity Grading Approach using Deep Soft Attention Networks and Segmentation of Chest X-ray Images

Inhalt:
The paper introduces an advanced AI-based approach for COVID-19 severity grading using chest X-ray images, featuring the integration of deep soft attention networks and segmentation techniques. The core of the approach lies in its utilization of a soft attention mechanism, which significantly enhances the model's ability to focus on the most critical features within the X-ray images. By dynamically adjusting its focus, the soft attention mechanism allows the model to prioritize regions that exhibit the most indicative patterns of COVID-19, such as lung opacities and other abnormalities. This selective attention not only improves the accuracy of the classification process but also ensures that the model is less susceptible to noise and irrelevant features present in the images. The attention mechanism works in conjunction with a U-Net architecture, which segments the lung regions to isolate areas most affected by the virus. The soft attention layer then further refines this by emphasizing the most relevant regions within these segmented areas, ensuring that the model accurately identifies the severity of the disease. With an accuracy rate of 87.33%, this approach demonstrates significant potential as a reliable diagnostic tool, aiding in rapid and informed decision-making in resource-constrained healthcare settings. This work not only contributes to the field of medical imaging but also provides a scalable solution that can be deployed in diverse healthcare settings, improving patient outcomes during the pandemic.