Citation :

M K V Anvesh, Prajna Bodapati, "Alzheimer’s Disease Classification using a Hybrid CNN-LSTM-Quantum Learning Approach," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 9, pp. 424-434, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I9P129

Abstract

Alzheimer’s disease is a neurodegenerative disease and a primary cause of dementia. Hence, an accurate early detection method for this disease is highly important. However, most existing methods rely on a medical evaluation/clinical study or single model learning methods that cannot address complex patterns, temporal associations, or class imbalance issues associated with Alzheimer’s MRI images effectively. In this regard, a CNN-LSTM-Quantum hybrid approach has been developed for the stage-wise classification of patients who have Alzheimer’s disease based on their brain MRI images. Spatial features within MRI images were extracted using a ResNet-18 architecture, while a two-layer LSTM architecture was used for recognizing sequential correlations within a sequence of MRI images. Moreover, a quantum-inspired classifier helps enhance feature extraction capabilities alongside decision-making processes. In this research, a fully balanced dataset was used, including data associated with four different stages of Alzheimer’s disease, allowing for an unconstrained analysis. The test accuracy of 98.9% was verified by the proposed approach, confirming that a high level of precision, recall, and class separability was reached.

Keywords

Alzheimer’s Disease, ResNet-18, LSTM, Feature fusion, Hybrid quantum-classical model, Medical image classification, Disease stage prediction.

References

[1] Jifa Zhang et al., “Recent Advances in Alzheimer’s Disease: Mechanisms, Clinical Trials and New Drug Development Strategies,” Signal Transduction and Targeted Therapy, vol. 9, no. 1, pp. 1-35, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[2] Alzheimer, “Alzheimer's Disease Facts and Figures,” Alzheimers Dementia, vol. 20, no. 5, pp. 3708-3821, 2024.
[CrossRef] [Google Scholar] [Publisher Link]   

[3] Bart De Strooper, and Eric Karran, “The Cellular Phase of Alzheimer's Disease,” Cell, vol. 164, no. 4, pp. 603-615, 2016.
[CrossRef] [Google Scholar] [Publisher Link]  

[4] Joseph Therriault et al., “Biomarker-based Staging of Alzheimer Disease: Rationale and Clinical Applications,” Nature Reviews Neurology, vol. 20, no. 4, pp. 232-244, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Michael T. Heneka et al., “Neuroinflammation in Alzheimer Disease,” Nature Reviews Immunology, vol. 25, no. 5, pp. 321-352, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Enjie Liu, Yao Zhang and Jian-Zhi Wang, “Updates in Alzheimer's Disease: From Basic Research to Diagnosis and Therapies,” Translational Neurodegeneration vol. 13, no. 1, pp. 1-48, 2024.  
[CrossRef] [Google Scholar] [Publisher Link]

[7] Jeffrey Cummings et al., “Alzheimer's Disease Drug Development Pipeline: 2024,” Alzheimer's and Dementia: Translational Research and Clinical Interventions, vol. 10, no. 2, pp. 1-22, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[8] Christopher H. van Dyck et al., “Lecanemab in Early Alzheimer’s Disease,” The New England Journal of Medicine, vol. 388, no. 1, pp. 9-21, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[9] C. Kavitha et al., “Early-Stage Alzheimer’s Disease Prediction using Machine Learning Models,” Frontiers in Public Health, vol. 10, pp. 1-13 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[10] Vivek K. Tiwari, Premananda Indic, and Shawana Tabassum, “A Study on Machine Learning Models in Detecting Cognitive Impairments in Alzheimer’s Patients using Cerebrospinal Fluid Biomarkers,” American Journal of Alzheimer's Disease and Other Dementias, vol. 39, pp. 1-5, 2024.  
[CrossRef] [Google Scholar] [Publisher Link]

[11] Mathias Holsey Gramkow et al., “Diagnostic Performance of Actigraphy in Alzheimer's Disease using a Machine Learning Classifier - a Cross-Sectional Memory Clinic Study,” Alzheimer's Research and Therapy, vol. 17, no. 1, pp. 1-12, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[12] Saranya Rathinam, Krishnan Nallaperumal, and Kalidass Subramaniam, “A Review of Recent Advances in Alzheimer’s Disease Machine Learning Algorithms for Early Mild Cognitive Impairment Prediction,” South Eastern European Journal of Public Health, vol. 26, pp. 1688-1703, 2025.
[CrossRef] [Publisher Link]

[13] Shweta Madiwalar et al., “Classification and Investigation of Alzheimer Disease using Machine Learning Algorithms,” Bioscience Biotechnology Research Communications, vol. 13, no. 13, pp. 15-20, 2020.
[CrossRef] [Google Scholar]

[14] Lilia Lazli, “Improved Alzheimer Disease Diagnosis with a Machine Learning Approach and Neuroimaging: Case Study Development,” JMIR Publications. Advancing Digital Health and Open Science, vol. 6, no. 1, pp. 1-13, 2025.
[CrossRef] [Google Scholar] [Publisher Link]  

[15] Archana Menon P, and R. Gunasundari, “SHAP-based Feature Selection and Explainable Machine Learning Classification of Alzheimer’s Disease,” Journal of Computational Analysis and Applications (JoCAAA), vol. 33, no. 6, pp. 798-817, 2024.
[Google Scholar] [Publisher Link]    

[16] Saddam Bekhet, Nagwa Saad, and Sara Farag, “Alzheimer Disease Predicting from Clinical and MRI Data using DeepALZNET Dual Pathway Framework,” Scientific Reports, vol. 15, no. 1, pp. 1-15, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Hanyu Wang, “Research on an AI Model for Early Alzheimer's Detection,” Applied and Computational Engineering, vol. 116, no. 1, pp. 168-173, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[18] Sara Basanta-Torres, Miguel Ángel Rivas-Fernández, and Santiago Galdo-Alvarez, “Artificial Intelligence for Alzheimer's Disease Diagnosis Through T1-Weighted MRI: A Systematic Review,” Computers in Biology and Medicine, vol. 197, pp. 1-18, 2025.
[CrossRef] [Google Scholar] [Publisher Link]  

[19] Akhilesh Deep Arya et al., “A Systematic Review on Machine Learning and deep Learning Techniques in the Effective Diagnosis of Alzheimer's Disease,” Brain Informatics, vol. 10, no. 1, pp. 1-15, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[20] Vanessa M. Young et al., “Data Leakage in Deep Learning for Alzheimer’s Disease Diagnosis: A Scoping Review of Methodological Rigor and Performance Inflation,” Diagnostics, vol. 15, no, 18, pp. 1-18, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[21] Maleika Heenaye-Mamode Khan et al., “Detection of Alzheimer’s Disease using Pre-Trained Deep Learning Models through Transfer Learning: A Review,” Artificial Intelligence Review, vol. 57, no. 10, pp. 1-53, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[22] A. Sundar Raj et al., “An Optimized Hybrid deep Learning Model to Detect Alzheimer Disease,” Scientific Reports, vol. 15, no. 1, pp. 1-20, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[23] Arshia Eftekhari Zadeh et al., “Detection of Autism Spectrum Disorder using Quantum Support Vector Machines Algorithm,” 2025 International Conference for Artificial Intelligence, Applications, Innovation and Ethics (AI2E), Muscat, Oman, pp. 1-6, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[24] Mustafa Lateef Fadhil Jumaili, and Emrullah Sonuç, “ML-Driven Alzheimer's Disease Prediction: A Deep Ensemble Modeling Approach,” SLAS Technol, vol. 32, pp. 1-15, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[25] Muhammad Umair Ali et al., “MRI-Driven Alzheimer’s Disease Diagnosis using Deep Network Fusion and Optimal Selection of Feature,” Bioengineering, vol. 11, no. 1, pp. 1-16, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[26] Anthony Chidubem Mmadumbu et al., “Early Detection of Alzheimer's Disease using Deep Learning Methods,” Alzheimers Dement, vol. 21 no. 5, pp. 1-12, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[27] Srijeet Goswami et al., “Alzheimer’s Disease Detection and Severity Analysis using Deep Learning Methods,” International Journal of Engineering Research and Technology (IJERT), Vol 14, no. 6, pp. 1-9, 2025.
[Google Scholar]