Enhanced Catch Fish Optimization Algorithm: Application in Multi-Threshold Segmentation for Gallbladder Cancer CT Scans

The present study aims to explore the application of enhanced-catch fish optimization algorithm (E-CFOA) in the field of CT imaging for early-stage diagnosis of gallbladder cancer, with the objective of enhancing patient survival rates. The study utilizes E-CFOA, a key component in the field of medical imaging, to enhance the efficiency of image segmentation, a crucial step in the diagnosis and treatment of gallbladder cancer. The study proposes a novel approach to image segmentation using E-CFOA, aiming to improve the accuracy and efficiency of CT scans for gallbladder cancer. The algorithm is based on a collaborative search mechanism that shares information. It simulates the behaviour of fishermen using their personal experience and working together as a team to catch fish. The algorithm's flexibility and adaptability are enhanced by individual and group fishing strategies, which improves the accuracy of the search. The experimental results demonstrate that E-CFOA has significant practical value and research potential in the segmentation of CT images of renal cell carcinoma. In comparison to other advanced optimisation algorithms, it demonstrates higher efficiency and segmentation accuracy.