🤖 AI Summary
This work proposes a novel approach to enhance the robustness of brain tumor classification models against adversarial perturbations by integrating non-negative matrix factorization (NMF), a lightweight convolutional neural network, and a diffusion-based denoising mechanism. The method leverages NMF in the feature space to extract interpretable representations and employs diffusion denoising to purify adversarial inputs, thereby significantly improving model robustness. Experimental results demonstrate that the proposed framework achieves competitive classification accuracy while substantially outperforming existing methods under strong adversarial attacks, as evaluated by AutoAttack. This approach effectively realizes a synergistic optimization of both accuracy and robustness in brain tumor MRI classification.
📝 Abstract
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 interpretable NNMF feature representations are extracted. Statistical metrics, including AUC, Cohen's d, and p-values, are used to rank and choose the most discriminative components. Then, a lightweight CNN classifier is trained directly on the selected feature groups. To improve adversarial robustness, a diffusion-based feature-space purification module is introduced. A forward noise method followed by a learned denoiser network is used before classification. System performance is estimated using both clean accuracy and robust accuracy under powerful adversarial attacks created by AutoAttack. The experimental results show that the proposed framework achieves competitive classification performance while significantly enhancing robustness against adversarial perturbations.The findings presuppose that combining interpretable NNMF-based representations with a lightweight deep approach and diffusion-based defense technique supplies an effective and reliable solution for medical image classification under adversarial conditions.