Bio-inspired computing algorithms in dementia diagnosis – a application-oriented review

Mandave, Deepa D.; Patil, Lalit V.

doi:10.1016/j.rico.2023.100276

Cited by 3 publications

(1 citation statement)

References 67 publications

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“…Particle swarm optimization (PSO) has emerged as a versatile and innovative optimization technique, with applications spanning various domains, including image analysis [62,63]. Initially conceived as an optimization method inspired by collective swarm behavior, PSO's adaptability, effectiveness, and problem-solving capabilities make it an increasingly influential tool in image analysis [64,65].…”

Section: Particle Swarm Optimization (Pso) In Image Analysismentioning

confidence: 99%

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Saifullah,

Dreżewski

2024

Applied Sciences

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Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study of the efficacy of particle swarm optimization (PSO) combined with histogram equalization (HE) preprocessing for medical image segmentation, focusing on lung CT scan and chest X-ray datasets. Best-cost values reveal the PSO algorithm’s performance, with HE preprocessing demonstrating significant stabilization and enhanced convergence, particularly for complex lung CT scan images. Evaluation metrics, including accuracy, precision, recall, F1-score/Dice, specificity, and Jaccard, show substantial improvements with HE preprocessing, emphasizing its impact on segmentation accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, and K-means, confirm the competitiveness of the PSO-HE approach, especially for chest X-ray images. The study also underscores the positive influence of preprocessing on image clarity and precision. These findings highlight the promise of the PSO-HE approach for advancing the accuracy and reliability of medical image segmentation and pave the way for further research and method integration to enhance this critical healthcare application.

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