Citation

Khan, Sunawar and Mazhar, Tehseen and Naz, Naila Sammar and Ahmed, Fahed and Shahzad, Tariq and Ali, Atif and Khan, Muhammad Adnan and Hamam, Habib (2025) Advanced Feature Selection Techniques in Medical Imaging—A Systematic Literature Review. Computers, Materials & Continua. pp. 1-10. ISSN 1546-2226

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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.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, hybrid approaches
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Others
Depositing User:	Nurin Syazwani Azmi
Date Deposited:	10 Nov 2025 00:43
Last Modified:	10 Nov 2025 00:43
URII:	http://shdl.mmu.edu.my/id/eprint/14788

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