Neuro Imaging Assisted Cognitive Disorder Detection

Cognitive disorders like Alzheimer’s disease and vascular dementia are global health concerns affecting millions of people worldwide. It is important to diagnose these disorders at an early stage to effectively intervene and provide treatment. This research proposes a complete computational framework for the detection of cognitive disorders using sophisticated deep learning and machine learning techniques. The proposed system is based on the combination of MRI analysis and CNN feature extraction VGG16 and Support Vector Machine (SVM) classification tech niques. This proposed system can classify Alzheimer’s disease into four different stages: No Impairment, Very Mild Impairment, Mild Impairment, and Moderate Impairment. Moreover, the pro posed system can classify the presence or absence of vascular de mentia. This proposed system includes an explanation mechanism using Grad-CAM to offer visual explanations for the predictions made bythe system. A Flask-based web application is proposed to offer user-friendly access to the proposed diagnostic system with complete reporting capabilities. Analysis of the proposed system shows promising results in terms of accuracy, precision, recall, and F1-measure values. This work is a significant contribution to the new area of medical image analysis.

Alzheimer’s Disease, Vascular Dementia, MRI, CNN, VGG16, Support Vector Machine, Grad-CAM.

1. Alam, MD Minul, and Shahram Latifi. ”A Systematic Review of Tech niques for Early-Stage Alzheimer’s Disease Diagnosis Using Machine Learning and Deep Learning.” Journal of Data Science and Intelligent Systems (2025). https://ojs.bonviewpress.com/index.php/jdsis/article/view/5037 [Google Scholar] [Crossref]

2. Alizadeh, Farzad, et al. ”Differential diagnosis among alzheimer’s disease, mild cognitive impairment, and normal subjects using resting state fMRI data extracted from multi subject dictionary learning atlas.” Frontiers in Biomedical Technologies 9.4 (2022): 297-306. https://publish.kne-publishing.com/index.php/fbt/article/view/10423 [Google Scholar] [Crossref]

3. Alruily, Meshrif, et al. ”Ensemble deep learning for Alzheimer’s disease diagnosis using MRI: Integrating features from VGG16, MobileNet, and InceptionResNetV2 models.” PloS one 20.4 (2025): e0318620. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0318620 [Google Scholar] [Crossref]

4. AV, AMBILI, DAS KUMAR, and DR LATIP. ”CNN-Mobilenetv2 Deep Learning-Based Alzheimer’s Disease Prediction and Classifica tion.” Journal of Theoretical and Applied Information Technology 101.9 (2023). http://www.jatit.org/volumes/Vol101No9/32Vol101No9.pdf [Google Scholar] [Crossref]

5. Awang, Mohd Khalid, et al. ”Classification of Alzheimer disease using DenseNet-201 based on deep transfer learning technique.” Plos one 19.9 (2024): e0304995. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0304995 [Google Scholar] [Crossref]

6. Basheer, Shakila, Surbhi Bhatia, and Sapiah Binti Sakri. ”Computational modeling of dementia prediction using deep neural network: analysis on OASIS dataset.” IEEE access 9 (2021): 42449-42462. https://ieeexplore.ieee.org/abstract/document/9380278/ [Google Scholar] [Crossref]

7. Chouliaras, Leonidas, and John T. O’Brien. ”The use of neuroimaging techniques in the early and differential diagnosis of dementia.” Molec ular Psychiatry 28.10 (2023): 4084-4097. [Google Scholar] [Crossref]

8. Lampe, Leonie, et al. ”Multiclass prediction of different dementia syn dromes based on multi-centric volumetric MRI imaging.” NeuroImage: Clinical 37 (2023): 103320. https://www.sciencedirect.com/science/article/pii/S2213158223000098 [Google Scholar] [Crossref]

9. Lee, Hyunwoo, et al. ”Brain imaging abnormalities in mixed Alzheimer’s and subcortical vascular dementia.” Canadian Journal of Neurological Sciences 50.4 (2023): 515-528. https://www.cambridge.org/core/journals/canadian-journal-of-neurological-sciences/article/brain-imaging-abnormalities-in-mixed-alzheimers-and-subcortical-vascular-dementia/62C806DD19FE77608FD91159799AA445 [Google Scholar] [Crossref]

10. Morgan, Amy Elizabeth, and Mark Tom´as Mc Auley. ”Vascular de mentia: from pathobiology to emerging perspectives.” Ageing research reviews 96 (2024): 102278. https://www.sciencedirect.com/science/article/pii/S1568163724000965 [Google Scholar] [Crossref]

11. Nithya, V. P., N. Mohanasundaram, and R. Santhosh. ”An early detection and classification of Alzheimer’s disease framework based on ResNet 50.” Current Medical Imaging 20.1 (2024): e250823220361. https://www.benthamdirect.com/content/journals/cmir/10.2174/1573405620666230825113344 [Google Scholar] [Crossref]

12. Sampath, R., and M. Baskar. ”Alzheimer’s Disease Prediction Using Fly-Optimized Densely Connected Convolution Neural Networks Based on MRI Images.” The Journal of Prevention of Alzheimer’s Disease 11.4 (2024): 1106-1121. https://www.sciencedirect.com/science/article/pii/S2274580724000372 [Google Scholar] [Crossref]

13. Sharma, Shagun, et al. ”A deep learning based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer Disease using MRI scans.” Measurement: Sensors 24 (2022): 100506. https://www.sciencedirect.com/science/article/pii/S2665917422001404 [Google Scholar] [Crossref]

14. Tufail, Hina, et al. ”Classification of vascular dementia on magnetic res onance imaging using deep learning architectures.” Intelligent Systems with Applications 22 (2024): 200388. https://www.sciencedirect.com/science/article/pii/S2667305324000632 [Google Scholar] [Crossref]

15. Yao, Zhaomin, et al. ”Artificial intelligence-based diagnosis of Alzheimer’s disease with brain MRI images.” European Journal of Radiology 165 (2023): 110934. https://www.sciencedirect.com/science/article/pii/S0720048X23002486 [Google Scholar] [Crossref]

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