Sunawar Khan1, Tehseen Mazhar1,2,*, Naila Sammar Naz1, Fahad Ahmed1, Tariq Shahzad3, Atif Ali4, Muhammad Adnan Khan5,*, Habib Hamam6,7,8,9
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2347-2401, 2025, DOI:10.32604/cmc.2025.066932
- 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… More >
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