Multi-Class Classification of Alzheimers Impairment and Dementia Stages using an Efficientnet-B0 Deep Learning Framework

Abstract

Alzheimer’s disease is a progressive neurodegenerative condition that leads to deterioration of memory and cognitive function, where early detection is critical as therapeutic interventions are most effective prior to extensive neuronal loss. Many existing deep learning approaches focus on binary or ternary classification schemes, which inadequately reflect the full clinical spectrum of cognitive impairment and dementia, thereby limiting their practical diagnostic relevance.This study proposes a lightweight deep learning framework based on EfficientNet-B0 for fine-grained eight-class Alzheimer’s staging, encompassing four levels of cognitive impairment and four levels of dementia. A structured preprocessing pipeline, including normalization, contrast enhancement, resizing, and targeted data augmentation, is employed to improve MRI consistency and enhance discriminative feature learning.Under a strict patient-level evaluation protocol, the proposed model achieves a peak testing accuracy of 99.78% and demonstrates strong and stable performance when compared with commonly used 2D convolutional neural network baselines. Owing to its low computational complexity and consistent performance across classes, the framework represents a promising research-stage tool for automated Alzheimer’s disease staging and warrants further validation, including comparison with transformer-based and volumetric approaches, before clinical deployment.