ArticleComputers in biology and medicine2022
Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images.
Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images.Computers in biology and medicine · 2023Article
- Gaussian bare-bone slime mould algorithm: performance optimization and case studies on truss structures.Artificial intelligence review · 2023Article
- Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images.Journal of bionic engineering · 2023Article
- Multi-Strategy Learning Boosted Colony Predation Algorithm for Photovoltaic Model Parameter Identification.Sensors (Basel, Switzerland) · 2022Article
- HADCNet: Automatic segmentation of COVID-19 infection based on a hybrid attention dense connected network with dilated convolution.Computers in biology and medicine · 2022Article
- Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation.Computers in biology and medicine · 2022Article
- Application of Improved Satin Bowerbird Optimizer in Image Segmentation.Frontiers in plant science · 2022Article
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9 authors.
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Abstract
The artificial bee colony algorithm (ABC) has been successfully applied to various optimization problems, but the algorithm still suffers from slow convergence and poor quality of optimal solutions in the optimization process. Therefore, in this paper, an improved ABC (CCABC) based on a horizontal search mechanism and a vertical search mechanism is proposed to improve the algorithm's performance. In addition, this paper also presents a multilevel thresholding image segmentation (MTIS) method based on CCABC to enhance the effectiveness of the multilevel thresholding image segmentation method. To verify the performance of the proposed CCABC algorithm and the performance of the improved image segmentation method. First, this paper demonstrates the performance of the CCABC algorithm itself by comparing CCABC with 15 algorithms of the same type using 30 benchmark functions. Then, this paper uses the improved multi-threshold segmentation method for the segmentation of COVID-19 X-ray images and compares it with other similar plans in detail. Finally, this paper confirms that the incorporation of CCABC in MTIS is very effective by analyzing appropriate evaluation criteria and affirms that the new MTIS method has a strong segmentation performance.
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