A Hybrid CNN-BILSTM Framework with Adaptive Learning for Early Detection of Alzheimer’s Disease

DOI

10.2991/978-94-6463-754-0_73How to use a DOI?

Keywords

Alzheimer’s disease; Deep Learning; CNN; Bi-LSTM; Adaptive Learning; Feature Extraction; Sequential Learning; Neurodegenerative Disorders; Medical Imaging; Early Diagnosis; Binary Classification

Abstract

Alzheimer’s disease (AD) is a neuro-degenerative disorder categorized by the progressive decline in cognitive function. Prompt and accurate detection is essential for both intervention and management of the disease. We present a Hybrid CNN-BiLSTM Framework with Adaptive Learning for early detection of AD in this work. Firstly, the model utilizes Convolutional Neural Networks (CNNs) for extraction of features from medical imaging data, capturing the disease-related spatial patterns. Then, a Bidirectional Long Short-Term Memory (Bi-LSTM) network is used to learn the sequential dependencies from the extracted features. To optimize the learning process and improve the performance of the classification, an adaptive learning mechanism is implemented that enables dynamic adjustment of various model parameters throughout the training phase. The evaluation of the proposed framework on several benchmark datasets shows that it outperforms traditional deep learning models in terms of accuracy and robustness. Early stages of AD can be diagnosed in this way, which may help in clinical decision-making and improve patients’ outcomes.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - V. Akila
AU  - S. Swetha
AU  - Harshini Elangovan
AU  - G. Gopika
PY  - 2025
DA  - 2025/06/30
TI  - A Hybrid CNN-BILSTM Framework with Adaptive Learning for Early Detection of Alzheimer’s Disease
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 837
EP  - 848
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_73
DO  - 10.2991/978-94-6463-754-0_73
ID  - Akila2025
ER  -