A Hybrid Deep Ensemble Model for Precise Liver and Tumor Segmentation Using U-Net and W-Net Architectures - B. Sravani; M. sunil Kumar (Journal of Electronics, Electromedical Engineering, and Medical Informatics, 2026)
The identification of the liver with the hepatic tumors on the computed tomography (CT) scans is a major compulsion to the earliest diagnosis, treatment planning, and surgery in the case of hepatocellular carcinoma. However, automated segmentation is not an easy job due to the non-homogeneous appearance of tumors, blurry boundaries, small size of annotated datasets, and high inter-slice variability. Existing single deep learning models are known to suffer from prediction variance and low generalization in complex clinical conditions. The primary goal of the study is to develop an effective, highly accurate segmentation model that improves the accuracy, consistency, and explanability of liver and tumor borders in CT images. In this paper, an original hybrid deep ensemble model is proposed that leverages the advantages of U-Net and W-Net. This is the primary contribution; one can combine the strong spatial localization ability of U-Net and the reconstruction-driven unsupervised learning ability of W-Net in minimizing the variance and maximizing the generalization. In addition, soft probability fusion, uncertainty modelling, and entropy-based confidence estimation are also introduced to improve reliability and clinical interpretation. The preprocessing of CT images is performed mathematically by normalizing and resizing to 256x256. U-Net and W-Net are trained separately using the pixel-wise probability maps, which are soft-averaged and thresholded. Benchmark liver CT datasets are tested with the ensemble using the Dice coefficient, accuracy, precision, recall, F1-score, Intersection over Union (IoU), ROC-AUC, and statistical significance tests. The results of the experiment show that the suggested ensemble performs better with an accuracy of 95.4, a precision of 94.3, a recall of 93.9, an F1-score of 94.1, IoU of 89.8, and an average ROC-AUC of 0.9615 than the models of the U-Net and W-Net, which differ in a huge number. Statistical confirmation that the improvements are relevant (p < 0.01) will be provided. In summary, the proposed deep ensemble segmentation can accurately, reliably, and effectively segment the liver and tumor, showing strong potential for clinical use and subsequent extension to multi-organ and multi-modal medical imaging.