International Journal of Emerging Research in Engineering, Science, and Management
Vol. 5, Issue 3, pp. 14-43, Jul-Sep 2026.
https://doi.org/10.58482/ijeresm.v5i3.2

Received: 22 Mar 2026 | Revised: 24 Jun 2026 | Accepted: 03 Jul 2026 | Published: 21 Jul 2026

This work is licensed under a Creative Commons Attribution 4.0 International License.

Improved Alzheimer's Disease Detection Using an Adaptive Spatial Attention-Based Twin Neural Network

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1Shaik Mahaboob Basha

2S. Swarnalatha

3B. Shoban Babu

1Ph.D. Scholar, Department of ECE, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, India.

2Professor, Department of ECE, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, India.

3Professor, Department of ECE, Sri Venkateswara College of Engineering, Tirupati, Andhra Pradesh, India.

Abstract: Alzheimer’s disease (AD) is one of the most prevalent forms of dementia, typically initiating with mild memory loss and progressively leading to severe cognitive decline. Early diagnosis is critical for effective intervention and improving patient outcomes. However, existing diagnostic approaches face challenges such as long prediction times, limited feature extraction capability, and the requirement for extensive training data, particularly in Machine Learning (ML)-based systems. To address these issues, this study proposes a novel computer-aided diagnostic framework called Adaptive Spatial Attention-based Twin Neural Network (ASTNet) for early AD detection using MRI data. Unlike traditional methods, ASTNet integrates several components: a biased fuzzy clustering-based segmentation technique that enhances tissue segmentation by emphasizing brain-relevant regions while suppressing skull and irrelevant tissues; Possibilistic C-Means (PCM) to improve segmentation robustness; a Convolutional Bidirectional LSTM (CBi-LSTM) network for context-aware feature extraction that captures temporal dependencies and reduces high-dimensional noise; and an adaptive spatial attention-based Twin Neural Network (TNN) for disease classification that improves spatial feature discrimination across cognitively normal (CN), mild cognitive impairment (MCI), and AD categories. The proposed approach was evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and achieved a classification accuracy of 0.9909, outperforming the compared Deep Learning (DL) models. Furthermore, the model demonstrates computational efficiency under the evaluated experimental setting and shows potential for computer-aided clinical decision support.

Keywords: Alzheimer’s disease diagnosis, Skull stripping, Biased fuzzy clustering, Bidirectional Long Short-Term Memory, Twin Neural Network.

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