Applications of the artificial bee colony algorithm in medical ‎imaging and diagnostics: a review

  • Authors

    • Awaz Ahmed Shaban Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, Iraq
    • Hajar Maseeh Yasin Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, Iraq

    Received date: January 10, 2025

    Accepted date: January 27, 2025

    Published date: February 7, 2025

    https://doi.org/10.14419/yszxm607
  • Artificial Bee Colony Algorithm; Metaheuristics; Swarm Intelligence; Optimization; Hybrid Algorithms.

  • Abstract

    The Artificial Bee Colony algorithm is an innovative optimization technique inspired by the foraging behavior of honeybees. Its ability to ‎balance exploration and exploitation makes it effective for addressing complex challenges, particularly in medicine. This paper explores its ‎applications in medical image segmentation, disease detection, and biomedical signal processing. Notable achievements include improving ‎tumor segmentation in noisy MRI scans and enhancing disease classification. However, challenges like high computational demands and ‎scalability remain. Hybrid approaches, such as combining ABC with neural networks, show promise. Future research could focus on real-‎time healthcare applications and integrating ABC with the Internet of Medical Things. This study underscores the potential of ABC to drive ‎significant advancements in healthcare‎.

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  • How to Cite

    Ahmed Shaban, A. ., & Yasin, H. M. . (2025). Applications of the artificial bee colony algorithm in medical ‎imaging and diagnostics: a review. International Journal of Scientific World, 11(1), 21-29. https://doi.org/10.14419/yszxm607

    Received date: January 10, 2025

    Accepted date: January 27, 2025

    Published date: February 7, 2025