A Multi-Level Cross-Connected U-Net Architecture for Image Segmentation

Abstract

Existing encoder-decoder architectures operating in the field of deep learning-based image segmentation face fundamental limitations such as information loss, performance degradation as network depth increases, and high computational costs. To overcome these issues, we propose a new architecture that integrates features from different depth levels at a single fusion point. This approach enables both comprehensive representation power and the preservation of very small details. The proposed approach creates an efficient structure that achieves high accuracy values without requiring unnecessary network deepening. The designed model was comprehensively compared with state-of-the-art architectures such as U-Net, V-Net, W-Net, T-Net, Seg-Net, and Multiple U-Net, which are accepted in the literature, on datasets with different characteristics such as MedSeg, Retina Drive, and Massachusetts datasets. Experimental findings reveal that the developed method outperforms its competitors in all test metrics. In particular, the dice (DSC) score, the most critical indicator of segmentation accuracy, achieved a value of 0.957 on the Retina DRIVE dataset, demonstrating a significant performance difference compared to existing models that remained in the 0.68-0.81 range in challenging scenarios. Furthermore, the 99.6% accuracy (Acc) and 0.006 loss (Loss) values obtained on COVID-19 CT data confirm the architecture's error-free learning capacity. The stable loss function trend observed across all datasets demonstrates the model's stable learning ability and high generalization capability.