Automatic segmentation of chest X-ray images via deep-improved various U-Net techniques

Accurate segmentation of medical images is vital for effective disease diagnosis and treatment planning. This is especially important in resource-constrained environments. This study aimed to evaluate the performance of various U-Net-based deep learning architectures for chest X-ray (CXR) segmentation and identify the most effective model in terms of both accuracy and computational efficiency.

We assessed the segmentation performance of eight U-Net variants: U-Net7, U-Net9, U-Net11, U-Net13, U-Net16, U-Net32, U-Net64, and U-Net128. The evaluation was conducted using a publicly available CXR dataset categorized into normal, COVID-19, and viral pneumonia classes. Each image was paired with a corresponding segmentation mask. Image preprocessing involved resizing, noise filtering, and normalization to standardize input quality. All models were trained under identical experimental conditions to ensure a fair comparison. Performance was evaluated using two key metrics: Intersection over Union (IoU) and Dice Coefficient (DC). Additionally, computational efficiency was measured by comparing the total number of trainable parameters and the training time for each model.

U-Net9 achieved the highest performance among all tested models. It recorded a DC of 0.98 and an IoU of 0.96, outperforming both shallower and deeper U-Net architectures. Models with increased depth or filter width, such as U-Net128, showed diminishing returns in accuracy. These models also incurred significantly higher computational costs. In contrast, U-Net16 and U-Net32 demonstrated reduced segmentation accuracy compared to U-Net9. Overall, U-Net9 provided the optimal balance between precision and computational efficiency for CXR segmentation tasks.

The U-Net9 architecture offers a superior solution for CXR image segmentation. It combines high segmentation accuracy with computational practicality, making it suitable for real-world applications. Its implementation can support radiologists by enabling faster and more reliable diagnoses. This can lead to improved clinical decision-making and reduced diagnostic delays. Future work will focus on integrating U-Net9 with multimodal imaging data, such as combining CXR with computerized tomography or MRI scans. Additionally, exploration of advanced architectures, including attention mechanisms and hybrid models, is planned to further enhance segmentation performance.