Frontiers in Biomedical Signal Processing
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TY - JOUR AU - Aluri, Pravallika AU - Parella, Venu Kumari AU - Thati, Jagadeesh PY - 2026 DA - 2026/03/01 TI - NES-Net: Neuro-Synergy Based Alzheimer’s Detection Using MRI Images JO - Frontiers in Biomedical Signal Processing T2 - Frontiers in Biomedical Signal Processing JF - Frontiers in Biomedical Signal Processing VL - 1 IS - 1 SP - 71 EP - 78 DO - 10.62762/FBSP.2025.463290 UR - https://www.icck.org/article/abs/FBSP.2025.463290 KW - Alzheimer’s disease KW - MRI KW - deep learning KW - neuro-synergy network KW - CNN KW - transformer KW - attention mechanism KW - medical image analysis AB - Alzheimer disease (AD) is a progressive neurodegenerative disorder that impairs memory and cognitive function in older adults, placing a substantial burden on global healthcare systems. Early and accurate diagnosis is crucial for timely intervention, yet manual interpretation of magnetic resonance imaging (MRI) scans is often subjective, time-consuming, and prone to inter-observer variability and bias. To overcome these challenges, we propose NES-Net (Neuro-Synergy Network), a novel deep learning model for automated Alzheimer's detection from MRI data. NES-Net employs a multi-branch hybrid architecture that simultaneously captures structural, spatial, and semantic features of brain images. By integrating convolutional neural network (CNN) modules for local pattern extraction with transformer-based attention mechanisms for global contextual understanding, the model effectively models both fine-grained details and long-range dependencies. These complementary representations are fused through a dedicated synergy fusion layer, inspired by inter-regional connectivity in human brain networks. Evaluated on benchmark public datasets including ADNI and OASIS, NES-Net outperforms traditional CNNs and state-of-the-art transformer-based models, achieving an overall accuracy of 94.83%, recall (sensitivity) of 93.92%, and AUC of 0.963. These results demonstrate that NES-Net, through its neuro-synergistic learning paradigm, offers a powerful, interpretable, and clinically valuable tool for early AD detection and decision support. SN - request pending PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Aluri2026NESNet,
author = {Pravallika Aluri and Venu Kumari Parella and Jagadeesh Thati},
title = {NES-Net: Neuro-Synergy Based Alzheimer’s Detection Using MRI Images},
journal = {Frontiers in Biomedical Signal Processing},
year = {2026},
volume = {1},
number = {1},
pages = {71-78},
doi = {10.62762/FBSP.2025.463290},
url = {https://www.icck.org/article/abs/FBSP.2025.463290},
abstract = {Alzheimer disease (AD) is a progressive neurodegenerative disorder that impairs memory and cognitive function in older adults, placing a substantial burden on global healthcare systems. Early and accurate diagnosis is crucial for timely intervention, yet manual interpretation of magnetic resonance imaging (MRI) scans is often subjective, time-consuming, and prone to inter-observer variability and bias. To overcome these challenges, we propose NES-Net (Neuro-Synergy Network), a novel deep learning model for automated Alzheimer's detection from MRI data. NES-Net employs a multi-branch hybrid architecture that simultaneously captures structural, spatial, and semantic features of brain images. By integrating convolutional neural network (CNN) modules for local pattern extraction with transformer-based attention mechanisms for global contextual understanding, the model effectively models both fine-grained details and long-range dependencies. These complementary representations are fused through a dedicated synergy fusion layer, inspired by inter-regional connectivity in human brain networks. Evaluated on benchmark public datasets including ADNI and OASIS, NES-Net outperforms traditional CNNs and state-of-the-art transformer-based models, achieving an overall accuracy of 94.83\%, recall (sensitivity) of 93.92\%, and AUC of 0.963. These results demonstrate that NES-Net, through its neuro-synergistic learning paradigm, offers a powerful, interpretable, and clinically valuable tool for early AD detection and decision support.},
keywords = {Alzheimer’s disease, MRI, deep learning, neuro-synergy network, CNN, transformer, attention mechanism, medical image analysis},
issn = {request pending},
publisher = {Institute of Central Computation and Knowledge}
}
Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits 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 licence, and indicate if changes were made. 
Frontiers in Biomedical Signal Processing
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Email: [email protected]
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