Enhanced Lung Segmentation via TransResUNet: A Deep Learning Approach with Residual Paths and VGG16 Encoder

Medical image analysis for diagnosing lung diseases heavily relies on automated lung segmentation as an essential procedure. The well-known UNet algorithms along with their variations have achieved results but encounter problems when handling complex and varied image characteristics. We develop TransResUNet as a deep learning architecture that connects VGG16 as its encoder with residual blocks to optimize both feature extraction and information propagation. Because our model uses residual paths as well as the Dice coefficient loss function it solves both class imbalance and vanishing gradient problems which allows for better segmentation results. TransResUNet surpasses baseline models according to extensive five-fold cross-validation experiments by reaching mean Dice coefficient 0.941 and mean IoU 0.896 and mean Binary Accuracy 0.983 along with peak values of 0.956, 0.914, and 0.987. The proposed model reaches superior segmentation performance while maintaining resistance against imaging problems that include both obstructions and reduced brightness levels. Application of TransResUNet demonstrates its value as an advanced and dependable method to automate clinical lung segmentation tasks that can create new opportunities for research in medical image segmentation.