3066-280XISSN (Online)
Volume 5 , Issue 1 , PP: 66-82, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Marwa Metwally 1 *
Doi: https://doi.org/10.54216/MOR.050104
This paper presents a comprehensive synthesis of recent advancements in the application of metaheuristic optimization algorithms for cancer detection, classification, and prediction. Drawing from a curated collection of studies spanning diverse cancer types including breast, lung, skin, cervical, oral, thyroid, and brain cancers, the work emphasizes how metaheuristics address challenges inherent to biomedical data, such as high dimensionality, noise, and limited sample sizes. A methodology table was developed to categorize each study by cancer domain, optimization method, and specific research task, enabling a comparative analysis of algorithmic patterns and hybridization strategies. The synthesis reveals that no single metaheuristic algorithm consistently outperforms others; instead, success depends on aligning algorithmic strengths with the characteristics of the diagnostic task and data. The discussion highlights the dominance of hybrid approaches, the emerging role of multi-objective optimization, the potential for cross-domain adaptation, and the necessity of addressing ethical, reproducibility, and clinical integration challenges. This work contributes both a structured reference and a roadmap for future research aimed at advancing computational oncology through strategic algorithm selection and design.
Metaheuristic Optimization , Feature Selection , Cancer Detection , Computational Oncology , Diagnostic Systems
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