An Effective Segmentation of MRI Images Combining Threshold and Hybrid Particle Swarm Optimization (HPSO-T) for Lung, Bone and Brain (LBB) DOI Open Access

N Raghapriya,

N Aswini,

G. Savitha

и другие.

International Journal of Electronics and Communication Engineering, Год журнала: 2024, Номер 11(12), С. 92 - 99

Опубликована: Дек. 31, 2024

Segmentation in medical imaging is one of the fundamental problems image processing. Perceptual completion and recognition during picture segmentation are issues with segmentation. Machine vision-based threshold an essential detecting tool. The issue time consumption arises traditional method. However, optimization techniques can help to resolve these problems. An effective technique needed determine ideal threshold. thresholding will become more computationally intensive increasing thresholds. This research proposed Hybrid Particle Swarm Optimization Thresholding (HPSO-T) used for assess MRI Image managing various tumors Lung, Brain Bone-(LBB). work extracts scan pictures using LBB data acquired from Kaggle website. suggested methodology outperforms other two approaches market a Dice Index 0.93.

Язык: Английский

Modified U-Net with attention gate for enhanced automated brain tumor segmentation DOI
Shoffan Saifullah, Rafał Dreżewski, Anton Yudhana

и другие.

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Янв. 2, 2025

Язык: Английский

Процитировано

1
Improved Brain Tumor Segmentation Using Modified U-Net based on Particle Swarm Optimization Image Enhancement DOI
Shoffan Saifullah, Rafał Dreżewski

Proceedings of the Genetic and Evolutionary Computation Conference Companion, Год журнала: 2024, Номер unknown, С. 611 - 614

Опубликована: Июль 14, 2024

Язык: Английский

Процитировано

8
Automatic Brain Tumor Segmentation Using Convolutional Neural Networks: U-Net Framework with PSO-Tuned Hyperparameters DOI
Shoffan Saifullah, Rafał Dreżewski

Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 333 - 351

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

5
Advanced brain tumor segmentation using DeepLabV3Plus with Xception encoder on a multi-class MR image dataset DOI
Shoffan Saifullah, Rafał Dreżewski, Anton Yudhana

и другие.

Multimedia Tools and Applications, Год журнала: 2025, Номер unknown

Опубликована: Фев. 21, 2025

Язык: Английский

Процитировано

0
ViT-CB: Integrating hybrid Vision Transformer and CatBoost to enhanced brain tumor detection with SHAP DOI
Radius Tanone, Li-Hua Li, Shoffan Saifullah

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 100, С. 107027 - 107027

Опубликована: Окт. 24, 2024

Язык: Английский

Процитировано

3
Wavelet Guided Visual State Space Model and Patch Resampling Enhanced U-shaped Structure for Skin Lesion Segmentation DOI Creative Commons

Shuwan Feng,

Xiaowei Chen, Shengzhi Li

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 181521 - 181532

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

1
Nonlinear crossing strategy-based particle swarm optimizations with time-varying acceleration coefficients DOI Creative Commons

Keigo Watanabe,

Xiongshi Xu

Applied Intelligence, Год журнала: 2024, Номер 54(13-14), С. 7229 - 7277

Опубликована: Июнь 3, 2024

Abstract In contemporary particle swarm optimization (PSO) algorithms, to efficiently explore global optimum solutions, it is common practice set the inertia weight monotonically decrease over time for stability, while allowing two acceleration coefficients, representing cognitive and social factors, adopt decreasing or increasing functions time, including random variations. However, there has been little discussion on a unified design approach these time-varying coefficients. This paper presents methodology designing monotonic construct nonlinear coefficients in PSO, along with control strategy exploring solutions. We first by linearly amplifying well-posed that increase normalized time. Here, ensure satisfaction of specified conditions at initial terminal points search process. many employed thus far only satisfy well-posedness either prompting proposal method adjust them virtually meet points. Furthermore, we propose crossing where developed intersect within interval, effectively guiding process pre-determining values times. The performance our Nonlinear Crossing Strategy-based Particle Swarm Optimization (NCS-PSO) evaluated using CEC2014 (Congress Evolutionary Computation 2014) benchmark functions. Through comprehensive numerical comparisons statistical analyses, demonstrate superiority seven conventional algorithms. Additionally, validate approach, particularly drone navigation scenario, through an example optimal 3D path planning. These contributions advance field PSO techniques, providing robust addressing complex problems.

Язык: Английский

Процитировано

0
Semi-Supervised and Class-Imbalanced Open Set Medical Image Recognition DOI Creative Commons

Yiqian Xu,

Ruofan Wang, Rui-Wei Zhao

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 122852 - 122877

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0
Improving YOLOv8 Performance Using Hyperparameter Optimization with Gray Wolf Optimizer to Detect Acute Lymphoblastic Leukemia DOI
Tanzilal Mustaqim, Chastine Fatichah, Nanik Suciati

и другие.

Опубликована: Сен. 12, 2024

Язык: Английский

Процитировано

0
An Effective Segmentation of MRI Images Combining Threshold and Hybrid Particle Swarm Optimization (HPSO-T) for Lung, Bone and Brain (LBB) DOI Open Access

N Raghapriya,

N Aswini,

G. Savitha

и другие.

International Journal of Electronics and Communication Engineering, Год журнала: 2024, Номер 11(12), С. 92 - 99

Опубликована: Дек. 31, 2024

Segmentation in medical imaging is one of the fundamental problems image processing. Perceptual completion and recognition during picture segmentation are issues with segmentation. Machine vision-based threshold an essential detecting tool. The issue time consumption arises traditional method. However, optimization techniques can help to resolve these problems. An effective technique needed determine ideal threshold. thresholding will become more computationally intensive increasing thresholds. This research proposed Hybrid Particle Swarm Optimization Thresholding (HPSO-T) used for assess MRI Image managing various tumors Lung, Brain Bone-(LBB). work extracts scan pictures using LBB data acquired from Kaggle website. suggested methodology outperforms other two approaches market a Dice Index 0.93.

Язык: Английский

Процитировано

0