Hybrid Fuzzy Logic and Metaheuristic Optimized Trinetfusion Model for Liver Tumor Segmentation

Liver tumor segmentation plays a vital role in medical imaging, enabling accurate diagnosis and precise treatment planning for liver cancer. Traditional methods such as threshold-based techniques and region-growing algorithms have been explored, and more recently, deep learning models have shown promise in automating and improving segmentation tasks. However, these approaches often face significant limitations, including challenges in accurately delineating tumor boundaries, high sensitivity to noise, and the risk of overfitting, especially when dealing with complex tumor structures and limited annotated data. To overcome these limitations, a novel Hybrid Fuzzy Logic and Metaheuristic Optimized TriNetFusion Model is proposed. This model integrates the strengths of fuzzy logic, metaheuristic optimization, and deep learning to deliver a more reliable and adaptable segmentation framework. Fuzzy logic is utilized to handle the inherent uncertainty and ambiguity in medical images, particularly in tumor boundary regions where intensity variations are subtle and complex. Metaheuristic optimization algorithms are employed to fine-tune the parameters of the segmentation model effectively, ensuring a more generalized and adaptive performance across different datasets. At the core of the model lies TriNetFusion, a multi-branch deep learning architecture that fuses complementary features extracted at various levels. The fusion of these multi-level features contributes to robust segmentation by capturing both global and local image characteristics. This model is specifically designed to adapt to irregular and complex tumor shapes, significantly reducing false positives and improving boundary precision. Experimental validation using benchmark liver tumor datasets demonstrates that the proposed model achieves a segmentation accuracy of 96% with a low loss value of 0.2, indicating strong generalization without overfitting. The hybrid approach not only enhances segmentation precision but also ensures robustness and adaptability, making it a highly promising solution for liver tumor segmentation in clinical practice.