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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer's Disease Diagnosis.

Saeka Rahman¹, Md Motiur Rahman¹, Smriti Bhatt²

¹School of Engineering Technology, Electrical and Computer Engineering Technology, Purdue University, West Lafayette, IN 47907, USA.

Bioengineering (Basel, Switzerland)

|October 29, 2025

Summary

NeuroNet-AD enhances Alzheimer's disease (AD) diagnosis by fusing MRI scans and clinical data. This novel deep learning model achieves high accuracy in classifying AD and mild cognitive impairment (MCI).

Keywords:

Alzheimer’s disease diagnosis CBAM MGCA deep learning multimodal fusion

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Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Alzheimer's disease (AD) is the most common dementia, impacting cognition and daily life.
Early diagnosis of AD and mild cognitive impairment (MCI) is crucial for patient care and treatment.
Current deep learning models for AD diagnosis face limitations due to data scarcity and challenges in fusing multimodal data.

Purpose of the Study:

To develop a novel multimodal deep learning framework, NeuroNet-AD, for enhanced Alzheimer's disease classification.
To improve diagnostic accuracy by integrating Magnetic Resonance Imaging (MRI) with clinical text-based metadata.
To address limitations of existing models in processing and fusing multimodal data for AD detection.

Main Methods:

Proposed NeuroNet-AD, a multimodal convolutional neural network (CNN) integrating MRI images and clinical metadata.
Incorporated Convolutional Block Attention Modules (CBAMs) into a ResNet-18 backbone for feature focus.
Introduced Meta Guided Cross Attention (MGCA) for effective cross-modal fusion using multi-head attention.
Employed an ensemble-based feature selection strategy for textual data.

Main Results:

NeuroNet-AD achieved 98.68% accuracy in multiclass (NC, MCI, AD) and 99.13% in binary (NC vs. AD) classification on the ADNI1 dataset.
External validation on OASIS-3 dataset showed 94.10% multiclass and 98.67% binary classification accuracy.
Demonstrated superior performance compared to state-of-the-art models in Alzheimer's disease diagnosis.

Conclusions:

NeuroNet-AD effectively integrates multimodal data (MRI and clinical text) for accurate Alzheimer's disease diagnosis.
The proposed framework, incorporating CBAMs and MGCA, significantly improves classification performance.
NeuroNet-AD shows strong generalization capabilities, outperforming existing methods and offering a promising tool for early AD detection.