Open Access

REVIEW

Advanced Feature Selection Techniques in Medical Imaging—A Systematic Literature Review

Sunawar Khan¹, Tehseen Mazhar^1,2,*, Naila Sammar Naz¹, Fahed Ahmed¹, Tariq Shahzad³, Atif Ali⁴, Muhammad Adnan Khan^5,*, Habib Hamam^6,7,8,9

1 School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
2 Department of Computer Science and Information Technology, School Education Department, Government of Punjab, Layyah, 31200, Pakistan
3 Department of Computer Engineering, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, 57000, Pakistan
4 Research Management Centre (RMC), Multimedia University, Cyberjaye Campus, 63100, Malaysia
5 Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, 13120, Republic of Korea
6 Faculty of Engineering, Uni de Moncton, Moncton, NB E1A3E9, Canada
7 School of Electrical Engineering, University of Johannesburg, Johannesburg, 2006, South Africa
8 International Institute of Technology and Management (IITG), Av. Grandes Ecoles, Libreville, BP 1989, Gabon
9 College of Computer Science and Engineering, University of Ha’il, Ha’il, 55476, Saudi Arabia

* Corresponding Authors: Tehseen Mazhar. Email: ; Muhammad Adnan Khan. Email:

(This article belongs to the Special Issue: Advanced Algorithms for Feature Selection in Machine Learning)

Computers, Materials & Continua 2025, 85(2), 2347-2401. https://doi.org/10.32604/cmc.2025.066932

Received 21 April 2025; Accepted 04 August 2025; Issue published 23 September 2025

Abstract

Feature selection (FS) plays a crucial role in medical imaging by reducing dimensionality, improving computational efficiency, and enhancing diagnostic accuracy. Traditional FS techniques, including filter, wrapper, and embedded methods, have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data. Deep learning-based FS methods, particularly Convolutional Neural Networks (CNNs) and autoencoders, have demonstrated superior performance but lack interpretability. Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution, offering improved accuracy and explainability. Furthermore, integrating multi-modal imaging data (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and Ultrasound (US)) poses additional challenges in FS, necessitating advanced feature fusion strategies. Multi-modal feature fusion combines information from different imaging modalities to improve diagnostic accuracy. Recently, quantum computing has gained attention as a revolutionary approach for FS, providing the potential to handle high-dimensional medical data more efficiently. This systematic literature review comprehensively examines classical, Deep Learning (DL), hybrid, and quantum-based FS techniques in medical imaging. Key outcomes include a structured taxonomy of FS methods, a critical evaluation of their performance across modalities, and identification of core challenges such as computational burden, interpretability, and ethical considerations. Future research directions—such as explainable AI (XAI), federated learning, and quantum-enhanced FS—are also emphasized to bridge the current gaps. This review provides actionable insights for developing scalable, interpretable, and clinically applicable FS methods in the evolving landscape of medical imaging.

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Keywords

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Supplementary Material

Supplementary Material File

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