Diffusion-Based Feature Denoising and Using NNMF for Robust Brain Tumor 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.

Figures (29)

• Figure S1: Stages of the proposed framework
• Figure S2: NNMF Basis Components (k = 15) . This figure shows the learned NNMF basis components got from the training data. Each basis image represents a non-negative spatial pattern that contributes to rebuilding brain MRI images. The components take meaningful anatomical structures such as skull boundaries, tissue distribution, and localized density variations. The variety across components indicates that NNMF decomposes the images into multiple complementary patterns rather than a single dominant structure, providing an interpretable and compact representation suitable for later analysis.
• Figure S3: Example Test Image and Its Normalized NNMF Feature Vector. This figure illustrates an example test MRI image from the normal class side, its matching normalized NNMF feature vector. The bar plot shows the activation power of each NNMF component for this image, highlighting that only a subset of components exhibits strong responses. This sparse and selective activation pattern explains that NNMF features encode discriminative structural information rather than uniformly responding to all components, which is desirable for robust classification.
• Figure S4: L2 Norm of Xtest After Normalization. This figure reports the L2 norm of all normalized test feature vectors. The values are tightly focused around one, confirming the right and stability of the normalization process. Ensuring unit-norm feature vectors is critical for fair comparison between samples and for robustness evaluation, as it blocks feature magnitude variations from controlling the classifier or adversarial attack.
• Figure S5: Class-wise Mean NNMF Features (Normalized, TEST). This figure shows the mean activation of each NNMF component for normal and tumor classes after feature normalization. Clearly, differences can be noticed via several components, where certain features show continuously, higher activation for tumor samples, while others are more prominent for normal samples. These class-dependent activation patterns indicate that NNMF components capture discriminative characteristics linked to pathological changes, supporting their effectiveness for classification and feature-space defense strategies.