# Lightweight Deep Learning Models with Explainable AI for Early Alzheimer’s Detection from Standard MRI Scans

## TL;DR

This paper introduces a lightweight deep learning model with explainable AI to detect early signs of Alzheimer's disease using standard MRI scans, aiming to improve early diagnosis in resource-limited settings.

## Contribution

The novelty lies in combining lightweight deep learning models with explainable AI techniques for early Alzheimer's detection using standard MRI scans.

## Key findings

- EfficientNetV2B0 achieved 88.0% mean accuracy in distinguishing between cognitive states in Alzheimer's progression.
- Explainability methods like Grad-CAM++ were integrated to visualize the anatomical basis of model predictions.
- The approach offers an accessible and interpretable tool for early Alzheimer's diagnosis in routine clinical settings.

## Abstract

Background: Dementia refers to a spectrum of clinical conditions characterized by impairments in memory, language, and cognitive function. Alzheimer’s Disease (AD) is the most common cause of dementia and it accounted for 60–70% of the estimated 57 million cases worldwide as of 2021. The exact pathology of this neurodegenerative condition is not fully understood. While it is currently incurable, progression to more critical stages can be slowed, and early diagnosis is crucial to alleviate and manage some of its symptoms. Contemporary diagnostic practices hinder early detection due to the high costs and inaccessibility of advanced neuroimaging tools and specialists, particularly for populations with resource-constrained clinical settings. Methods: This paper addresses this challenge by developing and evaluating computationally efficient lightweight deep learning models, MobileNetV2 and EfficientNetV2B0, for early AD detection from 2D slices sourced from standard structural magnetic resonance imaging (MRI). Results: For the challenging multi-class task of distinguishing between Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCI), and Late Mild Cognitive Impairment (LMCI), our best model, EfficientNetV2B0, achieved 88.0% mean accuracy across a 5-fold stratified cross-validation (std = 1.0%). To enhance clinical interpretability and build trust, we integrated explainability methods, Grad-CAM++ and Guided Grad-CAM++, to visualize the anatomical basis for the models’ predictions. Conclusions: This work delivers an accessible and interpretable neuroimaging tool to support early AD diagnosis and extend expert-level capabilities to routine clinical practice.