FOCUS-Net: A Hybrid Denoising and Confidence-Weighted Attention Fusion Framework for Robust Alzheimer’s Disease Classification from MRI Data

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder where early and accurate diagnosis from Magnetic Resonance Imaging (MRI) is critical for intervention. However, low signal-to-noise ratio, artifacts, and high inter-rater variability in clinical MRI scans pose significant challenges for automated diagnostic systems. This paper proposes FOCUS-Net, a novel end-to-end framework designed to enhance the robustness and interpretability of AD stage classification. Our approach integrates a hybrid denoising module, combining traditional filters (Wavelet, Gaussian, Anisotropic Diffusion, NLM) with a 3D U-Net CNN to suppress noise while preserving anatomical integrity. The cleaned images are processed by a diverse ensemble of 3D CNNs (ResNet-18, DenseNet-121, and a custom lightweight model). The core innovation is a novel confidence- and consistency-weighted fusion algorithm that dynamically aggregates ensemble predictions. Each model is weighted based on its predictive confidence (measured by the Shannon entropy of its output) and its spatial consistency (measured by the Dice similarity of its Grad-CAM attention mask with the ensemble consensus), balanced by a learnable parameter $\lambda$ . Preliminary experiments on the ADNI dataset demonstrate that FOCUS-Net achieves a classification accuracy of 88.9% and an AUC-ROC of 0.975, outperforming established baselines including a single model (80.0%), a simple averaging ensemble (84.4%), and fusion strategies using only confidence (86.7%) or only consistency (84.4%). The framework not only improves diagnostic accuracy but also provides interpretable visual explanations through consensus attention maps, offering a significant step towards reliable and trustworthy computer-aided diagnosis of Alzheimer’s disease.