LOOP RESIDUAL ATTENTION NETWORK FOR AUTOMATIC SEGMENTATION OF COVID-19 CHEST X-RAY IMAGES

In this research, a Loop Residual Attention Network was proposed for the automatic segmentation of COVID-19 infection regions in chest X-ray images. The proposed model integrates residual learning with attention mechanisms to enhance feature extraction and improve segmentation accuracy. Residual connections enable the network to learn deeper feature representations while addressing the vanishing gradient problem, and the attention modules guide the model to focus on the most relevant lung regions affected by infection. In addition, the loop-based architecture allows iterative refinement of segmentation outputs, leading to more precise localization of infected areas in chest X-ray images.   The experimental results demonstrate that the proposed model effectively identifies and segments COVID-19 infection regions, providing clear visualization of affected lung areas. The integration of attention mechanisms and residual learning significantly improves the performance of the segmentation process compared to conventional deep learning models. The system also shows strong generalization capability and can accurately analyze new chest X-ray images. By providing reliable segmentation maps, the proposed model can assist clin icians in diagnosing COVID-19 infections, monitoring disease progression, and supporting treatment planning.   Although the proposed approach shows promising results, there are several opportunities for future research. Future work may focus on expanding the model to analyze la rger and more diverse medical imaging datasets to improve its robustness and generalization capability. In addition, the model can be extended to work with other imaging modalities such as computed tomography (CT) scans for more detailed analysis of lung infections. Integrating advanced deep learning techniques such as transformer- based attention mechanisms and multi-scale feature learning may further improve segmentation accuracy. Moreover, implementing the system as a real-time clin ical decision support tool could assist healthcare professionals in rapid diagnosis and efficient management of respiratory diseases.

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