Metaheuristics in Deep Learning for 3D Medical Imaging

View Sample PDF

Author(s): A'adel Sayyahi (Shahid Chamran University of Ahvaz, Iran), Seyed Enayatallah Alavi (Shahid Chamran University of Ahvaz, Iran)and Morteza Jaderyan (Shahid Chamran University of Ahvaz, Iran)
Copyright: 2026
Pages: 42
Source title: Metaheuristic Algorithms and Optimizing Neural Networks for Biomedical Image Processing
Source Author(s)/Editor(s): Prasanalakshmi Balaji (King Khalid University, Saudi Arabia), K. Martin Sagayam (Karunya Institute of Technology and Sciences, India), Aditi Sharma (Symbiosis International University, India)and Korhen Cengiz (University of Fujairah, UAE)
DOI: 10.4018/979-8-3373-0523-3.ch012

Keywords: Computer Science & IT / Medical Information Science Reference / Neural Networks / Radiology and Imaging

Purchase

View Metaheuristics in Deep Learning for 3D Medical Imaging on the publisher's website for pricing and purchasing information.

Abstract

The integration of metaheuristic algorithms, such as genetic algorithms and particle swarm optimization, with deep learning techniques has significantly advanced 3D medical imaging, particularly through transfer learning. Transfer learning allows pre-trained models to be adapted to new tasks, addressing challenges posed by limited annotated datasets. Metaheuristic algorithms optimize deep learning models by refining hyperparameters and neural network architectures, enhancing accuracy and efficiency. This synergy improves model generalization, reduces computational costs, and tackles domain-specific challenges in medical imaging. Applications include tumor detection, organ segmentation, and diagnostics, with attention to ethical considerations and future research directions. This integration aims to develop more robust and efficient AI-driven solutions for medical imaging.

The IRMA Community

Research IRM

Metaheuristics in Deep Learning for 3D Medical Imaging

Purchase

Abstract

Related Content

IRMA Sponsors