Optimizing hyperparameters for dual-attention network in lung segmentation

Medical imaging, particularly chest X-rays (CXR), is a cornerstone in the diagnosis of lung diseases, such as pneumonia, tuberculosis and COVID-19, owing to its accessibility and effectiveness. However, the sheer volume of CXR images, especially during pandemics, combined with the complexity of subtle abnormalities, poses significant challenges for manual analysis. Lung segmentation plays a pivotal role in artificial intelligence-driven CXR analysis by isolating lung fields, which facilitates the detection of disease-affected regions. Recent advances in deep learning, particularly with attention mechanisms, have improved segmentation accuracy, but the performance of these models heavily depends on the selection of appropriate hyperparameters. This study investigates the impact of key hyperparameters—learning rate and number of epochs—on the performance of the dual-attention network (DANet) in lung segmentation tasks. DANet was tested on a CXR dataset from Qatar University and evaluated under four different hyperparameter configurations: 20 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.001, 10 epochs with a learning rate of 0.0001 and 20 epochs with a learning rate of 0.0001. The model's performance was assessed using two widely recognised segmentation metrics: the Dice coefficient and Intersection over Union (IoU). The results indicated that higher learning rates and greater numbers of epochs lead to improved segmentation performance. Specifically, the DANet model achieved a Dice coefficient of 97.29 % and an IoU value of 94.74 %, demonstrating its effectiveness compared to other models. These findings highlight the importance of hyperparameter tuning in achieving high segmentation accuracy and demonstrate the potential of the DANet model to improve diagnostic workflows for CXR analysis.