International Clinical Neuroscience Journal

Vol. 11 No. 1 (2024)

Mini-Review: Harnessing Swarm Intelligence for Early Alzheimer's Detection

International Clinical Neuroscience Journal, Vol. 11 No. 1 (2024), 4 November 2025 , Page e7
https://doi.org/10.22037/icnj.v11i1.47073

Abstract

Alzheimer’s Disease (AD), a leading cause of dementia, affects over 50 million people worldwide and is characterized by progressive cognitive decline, memory impairment, and behavioral changes. Early and accurate diagnosis remains critical for effective intervention, yet traditional methods often face challenges in scalability and precision. In this mini review, we evaluate the emerging role of Swarm Intelligence (SI) in the diagnosis of AD using Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) modalities. While traditional diagnostic methods suffer from limitations, SI-based algorithms, inspired by the collective behavior of biological swarms, are well-suited to manage complex datasets. Feature selection, parameter tuning, and pattern recognition benefit from techniques such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Bee Colony Algorithm (BCA). This review highlights SI’s computational efficiency, robustness, and transformative potential in improving diagnostic accuracy and scalability. We also address future challenges and research directions essential for integrating SI into real-world AD diagnostic systems. Ultimately, this work underscores SI’s promise in revolutionizing AD detection and enhancing patient outcomes.

Keywords:
  • Alzheimer's disease
  • Biological Signal Processing
  • Medical Image Processing
  • Machine Learning
  • Swarm Intelligence

How to Cite

1.
Mazrooei Rad E, Mazinani SM, Zendehbad SA. Mini-Review: Harnessing Swarm Intelligence for Early Alzheimer’s Detection. Int Clin Neurosci J [Internet]. 2025 Nov. 4 [cited 2026 Jul. 5];11(1):e7. Available from: https://journals.sbmu.ac.ir/neuroscience/article/view/47073

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