Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques DOI Creative Commons
Zhenjing Xie, Jinran Wu,

Weirui Tang

и другие.

PLoS ONE, Год журнала: 2024, Номер 19(3), С. e0297284 - e0297284

Опубликована: Март 21, 2024

Addressing the profound impact of Tapping Panel Dryness (TPD) on yield and quality in global rubber industry, this study introduces a cutting-edge Otsu threshold segmentation technique, enhanced by Dung Beetle Optimization (DBO-Otsu). This innovative approach optimizes combination accelerating convergence diversifying search methodologies. Following initial segmentation, TPD severity levels are meticulously assessed using morphological characteristics, enabling precise determination optimal thresholds for final segmentation. The efficacy DBO-Otsu is rigorously evaluated against mainstream benchmarks like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature (FSIM), compared with six contemporary swarm intelligence algorithms. findings reveal that substantially surpasses its counterparts image processing speed. Further empirical analysis dataset comprising cases from level 1 to 5 underscores algorithm’s practical utility, achieving an impressive 80% accuracy identification underscoring potential recognition tasks.

Язык: Английский

Liver Cancer Algorithm: A novel bio-inspired optimizer DOI
Essam H. Houssein, Diego Oliva, Nagwan Abdel Samee

и другие.

Computers in Biology and Medicine, Год журнала: 2023, Номер 165, С. 107389 - 107389

Опубликована: Авг. 30, 2023

Язык: Английский

Процитировано

156
An enhanced Coati Optimization Algorithm for global optimization and feature selection in EEG emotion recognition DOI
Essam H. Houssein, Asmaa Hammad,

Marwa M. Emam

и другие.

Computers in Biology and Medicine, Год журнала: 2024, Номер 173, С. 108329 - 108329

Опубликована: Март 19, 2024

Язык: Английский

Процитировано

17
An enhanced chameleon swarm algorithm for global optimization and multi-level thresholding medical image segmentation DOI
Reham R. Mostafa, Essam H. Houssein, Abdelazim G. Hussien

и другие.

Neural Computing and Applications, Год журнала: 2024, Номер 36(15), С. 8775 - 8823

Опубликована: Март 5, 2024

Язык: Английский

Процитировано

14
Multi-threshold Image Segmentation based on an improved Salp Swarm Algorithm: Case study of breast cancer pathology images DOI
Hongliang Guo, Mingyang Li,

Hanbo Liu

и другие.

Computers in Biology and Medicine, Год журнала: 2023, Номер 168, С. 107769 - 107769

Опубликована: Ноя. 28, 2023

Язык: Английский

Процитировано

20
Optimizing cancer diagnosis: A hybrid approach of genetic operators and Sinh Cosh Optimizer for tumor identification and feature gene selection DOI

Marwa M. Emam,

Essam H. Houssein, Nagwan Abdel Samee

и другие.

Computers in Biology and Medicine, Год журнала: 2024, Номер 180, С. 108984 - 108984

Опубликована: Авг. 10, 2024

Язык: Английский

Процитировано

8
An improved honey badger algorithm for global optimization and multilevel thresholding segmentation: real case with brain tumor images DOI
Essam H. Houssein,

Marwa M. Emam,

Narinder Singh

и другие.

Cluster Computing, Год журнала: 2024, Номер 27(10), С. 14315 - 14364

Опубликована: Июль 19, 2024

Язык: Английский

Процитировано

4
Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques DOI Creative Commons
Zhenjing Xie, Jinran Wu,

Weirui Tang

и другие.

PLoS ONE, Год журнала: 2024, Номер 19(3), С. e0297284 - e0297284

Опубликована: Март 21, 2024

Addressing the profound impact of Tapping Panel Dryness (TPD) on yield and quality in global rubber industry, this study introduces a cutting-edge Otsu threshold segmentation technique, enhanced by Dung Beetle Optimization (DBO-Otsu). This innovative approach optimizes combination accelerating convergence diversifying search methodologies. Following initial segmentation, TPD severity levels are meticulously assessed using morphological characteristics, enabling precise determination optimal thresholds for final segmentation. The efficacy DBO-Otsu is rigorously evaluated against mainstream benchmarks like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature (FSIM), compared with six contemporary swarm intelligence algorithms. findings reveal that substantially surpasses its counterparts image processing speed. Further empirical analysis dataset comprising cases from level 1 to 5 underscores algorithm’s practical utility, achieving an impressive 80% accuracy identification underscoring potential recognition tasks.

Язык: Английский

Процитировано

1