Journal of Trends in Computer Science and Smart Technology
IRO Journals
Volume 7 — Issue 3 — September 2025

Early Diagnosis of Alzheimer’s Disease Using Hybrid Deep Learning Model based on Integration of EfficientNet and LSTM

https://doi.org/10.36548/jtcsst.2025.3.006
Pages: 402-416
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Suvarna Vijaykumar Somvanshi
School of Computer Science and Engineering, Sandip University, Nashik
Prajkta P. Shirke
School of Computer Science and Engineering, Sandip University, Nashik
September 2025
Volume 7 — Issue 3
Journal of Trends in Computer Science and Smart Technology
PDF

How to Cite

Somvanshi, Suvarna Vijaykumar, and Prajkta P. Shirke. 2025. “Early Diagnosis of Alzheimer’s Disease Using Hybrid Deep Learning Model Based on Integration of EfficientNet and LSTM”. Journal of Trends in Computer Science and Smart Technology 7 (3): 402-16. https://doi.org/10.36548/jtcsst.2025.3.006.
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Keywords

— Alzheimer’s Disease
— Early Detection
— Disease Classification
— Diagnosis
— EfficientNet
— LSTM
— Deep Learning
Published: 02-09-2025

Abstract

Early detection of Alzheimer’s disease (AD) is crucial for prompt clinical intervention and enhanced patient outcomes. Here, we introduce a hybrid deep learning design that leverages EfficientNet-B6 spatial feature extraction capabilities and integrates the Long Short-Term Memory (LSTM) neural network to capture the sequential patterns in MRI scans for Alzheimer’s classification. The model is developed to categorize brain MRI scans into four diagnostic stages: non-demented, very mild demented, mild demented, and moderate demented, utilizing the publicly accessible augmented Alzheimer MRI datasets. A preprocessing step is applied, comprising scaling, normalization, and data augmentation, to ensure standardized and high-quality inputs to the network. The experimental findings for accuracy are presented, while we outline the conceptual basis of the hybrid model and its potential to address shortcomings of current methods, like inadequate spatial-temporal integration, scalability, and interpretability. The proposed architecture aims to enhance diagnostic results by leveraging EfficientNet-B6 multiscale spatial feature learning and LSTM temporal modeling. The proposed hybrid architecture evaluated on 80:20 training and validation dataset split, achieving a classification accuracy of 98.90% on a benchmark dataset, which shows the potential of the proposed hybrid architecture. This study lays the foundation for a comprehensive, interpretable, and scalable deep learning model for early AD detection from MRI scans.

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