.png){kind=icon}
Return
Dynamic comprehensive learning-based dung beetle optimizer using triangular mutation for polyps image segmentation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compbiolchem.2025.108474.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The process of early diagnosis of polyps is considered a critical point of preventive healthcare, as it can significantly improve the prognosis and treatment outcomes of patients with colorectal cancer. Since the Polyps are constructed in the colon, they can grow up to be cancerous over time. We present an alternative Polyps image segmentation approach according to multilevel thresholding techniques (MLTs) to ensure effective early polyp diagnosis. The developed MLT Polyps image segmentation method depends on improving the performance of the Dung beetle optimizer (DBO) algorithm based on the operators of Triangular Mutation (TMO), Comprehensive learning (CL), and dynamic update of the search domain. We conducted a comprehensive evaluation of the performance of the DCTDBO using eight Polyps images and compared it with other image segmentation methods, ensuring a rigorous and thorough process. The results show the high ability of the DCTDBO according to performance measures to segment the Polyps images. In terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity (FSIM), DCTDBO performed better than the basic version of the DBO and other methods. For example, the average of DCTDBO overall threshold levels and tested images in terms of FSIM, SSIM, and PSNR is 0.9668, 0.99217, and 28.9338, respectively. This indicates the influence of TMO and CL on enhancing the performance of DBO.
Keywords:
Polyps images
Image segmentation
Multilevel thresholding
Dung beetle optimizer (DBO)
Journal
IF:
4.8
Papers:
7.1K
Citations:
6.1W
Organization
View more organizations
Cited Papers
ShareSaveComprehensive learning particle swarm optimizer for global optimization of multimodal functions
PREAI
ShareSaveShareSaveShareSaveDung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification.png)
Cancers
OAAI
CancersIF0
OAAIShareSaveShareSaveShareSave
View more