ResNet50-Boosted UNet for Improved Liver Segmentation Accuracy

Abstract

Segmentation of the liver from abdominal CT images is difficult due to changes in form, density, and the presence of malignancies. This research describes a novel strategy to improve segmentation accuracy that uses UNet as a foundation architecture and ResNet50 as a backbone architecture. This integrated design automates feature selection and spatial awareness, overcoming limitations in previous models. Experimental evaluations using the LiTS dataset show higher performance. Specifically, using the LiTS dataset, our algorithm achieves a remarkable foreground accuracy of 99.81% in liver segmentation. These results outperform existing approaches, demonstrating UNet and ResNet50's potential as valuable tools for precise liver segmentation in clinical situations. The suggested system shows promise for application in diverse medical imaging tasks other than liver segmentation, demonstrating its versatility and effectiveness in enhancing machine-assisted medical diagnostics and decision-making processes.