Diffusion-Based Feature Denoising and Using NNMF for Robust Brain Tumor Classification
Brain tumor classification from magnetic resonance imaging, which is also known as MRI, plays a sensitive role in computer-assisted diagnosis systems. In recent years, deep learning models have achieved high classification accuracy. However, their sensitivity to adversarial perturbations has become an important reliability concern in medical applications. This study suggests a robust brain tumor classification framework that combines Non-Negative Matrix Factorization (NNMF or NMF), lightweight convolutional neural networks (CNNs), and diffusion-based feature purification. Initially, MRI images are preprocessed and converted into a non-negative data matrix, from which compact and interpret...
| Authors | Hiba Adil Al-kharsan, Róbert Rajkó |
|---|---|
| Primary category | cs.CV |
| Published | 13 March 2026 |
| Last updated at source | 7 April 2026 |
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