Published Paper Details:

This paper presents an enhanced approach for detecting and assessing the severity of COVID-19 from chest X-ray (CXR) images using a modified VGG16 architecture integrated with an attention mechanism. The attention layer, placed strategically after the Conv4_3 layer, enables the network to focus on critical regions, improving sensitivity and specificity. The model was trained on a combined dataset, achieving an accuracy of 94% and a Root Mean Square Error (RMSE) of 0.95 for severity prediction. These results highlight the model's superior performance in distinguishing between COVID-19 infected and normal cases and in providing accurate severity assessments. Comparative analyses demonstrate the model's efficacy over other retrained models, ensuring minimal false positives and accurate COVID-19 detection. This modified VGG16 model represents a significant advancement in medical image analysis, offering a reliable tool for real-time clinical applications in diagnosing and managing COVID-19.