A Dual-Model Deep Learning Framework for Early Alzheimer’s Detection Using Clinical Data and Neuroimaging with Architectural Performance Analysis

Year : 2026 | Volume : 16 | Issue : 01 | Page : 1 12
    By

    Abha Jain,

  • Sohan Lal Gupta,

  • Megha Gupta,

  • Mithlesh Arya,

  • Veena Yadav,

  1. Assistant Professor, Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology Management & Gramothan, Jaipur, Rajasthan, India
  2. Assistant Professor, Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology Management & Gramothan, Jaipur, Rajasthan, India
  3. Associate Professor, Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology Management & Gramothan, Jaipur, Rajasthan, India
  4. Associate Professor, Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology Management & Gramothan, Jaipur, Rajasthan, India
  5. Professor, Department of Computer Science and Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India

Abstract

Alzheimer’s disease (AD) poses a significant global health challenge due to its increasing prevalence and the absence of definitive cures. Early diagnosis is crucial for effective intervention and management. This study presents a dual-model deep learning framework for the early detection and classification of AD using both structured clinical data and neuroimaging datasets. Model 1 utilizes a greedy layer-wise autoencoder approach applied to structured data, achieving optimal binary classification accuracy of 95.8% with a four-layer configuration. Model 2 employs EfficientNet-B0 via transfer learning to classify MRI brain scans across multiple AD stages, reaching accuracies of up to 94%. Comparative analysis with existing state-of-the-art models validates the effectiveness of both approaches. Additionally, this paper highlights how network architecture, depth, and input modality significantly impact diagnostic performance. The findings advocate for the strategic design of AI models tailored to clinical applications and set the foundation for future multimodal, interpretable, and scalable Alzheimer’s diagnostic tools.

Keywords: Autoencoder, clinical data analysis, convolutional neural network, deep learning, early detection, efficientnet, medical diagnostics, MRI brain imaging, neural network architecture, neuroimaging, transfer learning

[This article belongs to Research and Reviews: A Journal of Neuroscience ]

How to cite this article:
Abha Jain, Sohan Lal Gupta, Megha Gupta, Mithlesh Arya, Veena Yadav. A Dual-Model Deep Learning Framework for Early Alzheimer’s Detection Using Clinical Data and Neuroimaging with Architectural Performance Analysis. Research and Reviews: A Journal of Neuroscience. 2026; 16(01):1-12.
How to cite this URL:
Abha Jain, Sohan Lal Gupta, Megha Gupta, Mithlesh Arya, Veena Yadav. A Dual-Model Deep Learning Framework for Early Alzheimer’s Detection Using Clinical Data and Neuroimaging with Architectural Performance Analysis. Research and Reviews: A Journal of Neuroscience. 2026; 16(01):1-12. Available from: https://journals.stmjournals.com/rrjons/article=2026/view=237397


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Regular Issue Subscription Review Article
Volume 16
Issue 01
Received 29/01/2026
Accepted 02/02/2026
Published 23/02/2026
Publication Time 25 Days
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