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用于COVID-19 X射线图像图像分割的水平和垂直搜索人工蜂群 - ScienceDirect
Source: sciencedirect.com
Article summary:
1. This paper proposes an improved artificial bee colony optimization (CCABC) for COVID-19 multi-threshold image segmentation based on two-dimensional histograms.
2. CCABC has greatly improved its ability to search for the best solutions.
3. The performance of CCABC and the improved image segmentation method have been verified by comparison with well-known algorithms.
Article analysis:
The article is generally reliable and trustworthy, as it provides a detailed description of the proposed algorithm and its application to COVID-19 X-ray images, as well as a comparison with other similar algorithms. The authors also provide evidence to support their claims, such as the use of 15 benchmark functions to compare CCABC with 30 other similar algorithms, and the use of appropriate evaluation criteria to confirm that CCABC is effective when incorporated into MTIS.
However, there are some potential biases in the article that should be noted. For example, the authors do not explore any counterarguments or present both sides equally; instead, they focus solely on presenting their own proposed algorithm and its advantages over existing methods. Additionally, there is no discussion of possible risks associated with using this algorithm or any potential drawbacks that could arise from its implementation. Finally, while the article does not contain any promotional content, it does emphasize the advantages of CCABC over other methods without providing sufficient evidence to back up these claims.