VISUALIZING BIOMARKER SIGNATURES: EXPLAINABLE DEEP LEARNING FOR EARLY DETECTION OF ALZHEIMER’S AND NEURODEGENERATIVE DISORDERS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i2s.2026.7266Keywords:
Neurodegenerative Diseases, Alzheimer’s Disease, Explainable Artificial Intelligence (XAI), Deep Learning, Biomarker Identification, Multimodal NeuroimagingAbstract [English]
The timely and precise diagnosis of neurodegenerative diseases, especially Alzheimer disease and related disorders, is a major clinical issue because of the subtle changes in the pathology of the disease at the early stage, variable disease progression, and low interpretability of the current artificial intelligence based systems of diagnosis. To overcome these obstacles, this paper introduces XAI-BioNet, which is a more explainable deep learning network, allowing a combination of state-of-the-art feature selection, clear biomarkers discovery, and strict laboratory validation of early neurodegenerative disease detection. XAI-BioNet uses the multimodal data including structural MRI, longitudinal cognitive functions, and demographic characteristics. Complementary architectures, such as VGG and MobileNetV2 to learn deep features through hierarchical and efficient learning of spatial features on the basis of neuroimaging data and LSTM networks to learn temporal dynamics in cognitive progression are used to perform the deep feature extraction. The dimensionality of features and redundancy are covered by the principal component analysis and the recursive elimination of features that makes sure to retain biomarkers that are clinically relevant. SHAP and Grad-CAM allow obtaining model interpretability and making transparent attribution of the diagnostic decision and neuroanatomical relevance. The comprehensiveness of validation is carried out on heterogeneous, real world clinical data with stratified cross-validation and cross-cohort evaluation undertaken to determine the robustness, scalability and generalizability. The recommended XAI-BioNet framework has a 96.2% diagnostic accuracy, 94.7% sensitivity, 97.1% specificity, and AUC= 0.97, and it is more effective than standalone VGG (89.6% accuracy), MobileNetV2 (91.8% accuracy), and LSTM (92.9% accuracy) models. The findings emphasize the possibility of XAI-BioNet being a clinically understandable and scalable model of early diagnosis of neurodegenerative diseases.
References
Alam, M. M., and Latifi, S. (2025). Early detection of Alzheimer’s Disease Using Generative Models: A Review of GANs and Diffusion Models in Medical Imaging. Algorithms, 18(7), 434. https://doi.org/10.3390/a18070434 DOI: https://doi.org/10.3390/a18070434
AlMansoori, M. E., Jemimah, S., Abuhantash, F., et al. (2024). Predicting Early Alzheimer’s With Blood Biomarkers and Clinical Features. Scientific Reports, 14, 6039. https://doi.org/10.1038/s41598-024-56489-1 DOI: https://doi.org/10.1038/s41598-024-56489-1
Alqahtani, S., Alqahtani, A., Zohdy, M. A., Alsulami, A. A., and Ganesan, S. (2023). Severity Grading and Early Detection of Alzheimer’s Disease Through Transfer Learning. Information, 14, 646. https://doi.org/10.3390/info14120646 DOI: https://doi.org/10.3390/info14120646
Cabrini, G. B., Schoen, P., Consolo, E. D., and Gonçalves, C. (2025). Biochemical Markers for Early Detection of Dementia: A Review of Current Advances. Asclepius International Journal of Scientific and Health Sciences, 4(6), 104–114. https://doi.org/10.70779/aijshs.v4i6.142 DOI: https://doi.org/10.70779/aijshs.v4i6.142
Cardinali, L., Mariano, V., Rodriguez-Duarte, D. O., Tobón Vasquez, J. A., Scapaticci, R., Crocco, L., and Vipiana, F. (2025). Early Detection of Alzheimer’s Disease Via Machine Learning-Based Microwave Sensing: An Experimental Validation. Sensors, 25(9), 2718. https://doi.org/10.3390/s25092718 DOI: https://doi.org/10.3390/s25092718
Dubois, B., von Arnim, C. A. F., Burnie, N., et al. (2023). Biomarkers in Alzheimer’s Disease: Role in Early and dIfferential Diagnosis and Recognition of Atypical Variants. Alzheimer’s Research and Therapy, 15, 175. https://doi.org/10.1186/s13195-023-01314-6 DOI: https://doi.org/10.1186/s13195-023-01314-6
Flanagan, K., and Saikia, M. J. (2023). Consumer-Grade Electroencephalogram and Functional Near-Infrared Spectroscopy Neurofeedback Technologies for Mental Health and Wellbeing. Sensors, 23, 8482. https://doi.org/10.3390/s23208482 DOI: https://doi.org/10.3390/s23208482
Gao, X., Shi, F., Shen, D., and Liu, M. (2022). Task-Induced Pyramid and Attention GAN for Multimodal Brain Image Imputation and Classification in Alzheimer’s Disease. IEEE Journal of Biomedical and Health Informatics, 26, 36–43. https://doi.org/10.1109/JBHI.2021.3097721 DOI: https://doi.org/10.1109/JBHI.2021.3097721
Grover, P., Chaturvedi, K., Zi, X., Saxena, A., Prakash, S., Jan, T., and Prasad, M. (2023). Ensemble Transfer Learning for Distinguishing Cognitively Normal and Mild Cognitive Impairment Patients Using MRI. Algorithms, 16, 377. https://doi.org/10.3390/a16080377 DOI: https://doi.org/10.3390/a16080377
Janghel, R. R., and Rathore, Y. K. (2021). Deep Convolution Neural Network Based System for Early Diagnosis of Alzheimer’s Disease. IRBM, 42, 258–267. https://doi.org/10.1016/j.irbm.2020.06.006 DOI: https://doi.org/10.1016/j.irbm.2020.06.006
Kandiah, N., Choi, S. H., Hu, C.-J., Ishii, K., Kasuga, K., and Mok, V. C. T. (2022). Current and Future Trends in Biomarkers for the Early Detection of Alzheimer’s Disease in Asia: Expert Opinion. Journal of Alzheimer’s Disease Reports, 6(1), 699–710. https://doi.org/10.3233/ADR-220059 DOI: https://doi.org/10.3233/ADR-220059
Kishore, P., Kumari, C. U., Kumar, M., and Pavani, T. (2021). Detection and Analysis of Alzheimer’s Disease Using Various Machine Learning Algorithms. Materials Today: Proceedings, 45, 1502–1508. https://doi.org/10.1016/j.matpr.2020.07.645 DOI: https://doi.org/10.1016/j.matpr.2020.07.645
Loued-Khenissi, L., Döll, O., and Preuschoff, K. (2019). An Overview of Functional Magnetic Resonance Imaging Techniques for Organizational Research. Organizational Research Methods, 22, 17–45. https://doi.org/10.1177/1094428118802631 DOI: https://doi.org/10.1177/1094428118802631
Pan, J., and Wang, S. (2022). Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer’s Disease.
Sekimori, T., Fukunaga, K., Finkelstein, D. I., and Kawahata, I. (2024). Advances in Blood Biomarkers and Diagnosis Approaches for Neurodegenerative Dementias and Related Diseases. Journal of Integrative Neuroscience, 23(10), 188. https://doi.org/10.31083/j.jin2310188 DOI: https://doi.org/10.31083/j.jin2310188
Singh, S. G., Das, D., Barman, U., and Saikia, M. J. (2024). Early Alzheimer’s Disease Detection: A Review of Machine Learning Techniques for Forecasting Transition from Mild Cognitive Impairment. Diagnostics, 14, 1759. https://doi.org/10.3390/diagnostics14161759 DOI: https://doi.org/10.3390/diagnostics14161759
Warren, S. L., and Moustafa, A. A. (2023). Functional Magnetic Resonance Imaging, Deep Learning, and Alzheimer’s Disease: A Systematic Review. Journal of Neuroimaging, 33, 5–18. https://doi.org/10.1111/jon.13063 DOI: https://doi.org/10.1111/jon.13063
Xie, Y., Zhang, P., and Zhao, J. (2023). A Spectral Sampling Algorithm in Dynamic Causal Modelling for Resting-State fMRI. Human Brain Mapping, 44, 2981–2992. https://doi.org/10.1002/hbm.26256 DOI: https://doi.org/10.1002/hbm.26256
Yue, J.-H., Zhang, Q.-H., Yang, X., Wang, P., Sun, X.-C., Yan, S.-Y., Li, A., Cao, D.-N., Wang, Y., Wei, Z.-Y., et al. (2023). Magnetic Resonance Imaging of White Matter in Alzheimer’s Disease: A Global Bibliometric Analysis from 1990 to 2022. Frontiers in Neuroscience, 17, 1163809. https://doi.org/10.3389/fnins.2023.1163809 DOI: https://doi.org/10.3389/fnins.2023.1163809
Yu, W., Lei, B., Ng, M. K., Cheung, A. C., Shen, Y., and Wang, S. (2020). Tensorizing GAN with High-Order Pooling for Alzheimer’s Disease Assessment.
Zhao, X., Ang, C. K. E., Acharya, U. R., and Cheong, K. H. (2021). Application of Artificial Intelligence Techniques for the Detection of Alzheimer’s Disease using Structural MRI Images. Biocybernetics and Biomedical Engineering, 41, 456–473. https://doi.org/10.1016/j.bbe.2021.02.006 DOI: https://doi.org/10.1016/j.bbe.2021.02.006
Zhao, Y., Guo, Q., Zhang, Y., Zheng, J., Yang, Y., Du, X., Feng, H., and Zhang, S. (2023). Application of Deep Learning for Prediction of Alzheimer’s Disease in PET/MR Imaging. Bioengineering, 10, 1120. https://doi.org/10.3390/bioengineering10101120 DOI: https://doi.org/10.3390/bioengineering10101120
Zia Ul Haq, M., Zhao, X., Obeng Apori, S., Singh, B., and Tian, F. (2025). Molecular Biomarkers for Early Detection of Alzheimer’s Disease and the Complementary Role of Engineered Nanomaterials: A Systematic Review. International Journal of Molecular Sciences, 26(19), 9282. https://doi.org/10.3390/ijms26199282 DOI: https://doi.org/10.3390/ijms26199282
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Dr. Ravindra Keshav Moje, Sai Kiran Oruganti, Shakir Khan, Dr. Santosh H. Lavate
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
