Enhanced Liver Tumor Detection in CT Images Using 3D U-Net and Bat Algorithm for Hyperparameter Optimization

TL;DR

This paper presents a novel method combining 3D U-Net and Bat Algorithm to improve liver tumor segmentation accuracy in CT images, aiding early diagnosis and treatment planning.

Contribution

It introduces an integrated approach that optimizes hyperparameters of a 3D U-Net using the Bat Algorithm, enhancing segmentation performance over existing methods.

Findings

Achieved high F1-score in tumor segmentation

Demonstrated improved robustness and accuracy

Validated on publicly available dataset

Abstract

Liver cancer is one of the most prevalent and lethal forms of cancer, making early detection crucial for effective treatment. This paper introduces a novel approach for automated liver tumor segmentation in computed tomography (CT) images by integrating a 3D U-Net architecture with the Bat Algorithm for hyperparameter optimization. The method enhances segmentation accuracy and robustness by intelligently optimizing key parameters like the learning rate and batch size. Evaluated on a publicly available dataset, our model demonstrates a strong ability to balance precision and recall, with a high F1-score at lower prediction thresholds. This is particularly valuable for clinical diagnostics, where ensuring no potential tumors are missed is paramount. Our work contributes to the field of medical image analysis by demonstrating that the synergy between a robust deep learning architecture and a metaheuristic optimization algorithm can yield a highly effective solution for complex segmentation tasks.