Keywords
Convolutional Neural Networks
Magnetic Resonance Imaging.
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Abstract
Objective: To propose an intelligent algorithm for diagnosing Alzheimer's Disease using magnetic resonance imaging (MRI), as well as to compare the performance of five convolutional neural network models (ResNet50, VGG19, DenseNet201, InceptionV3, and EfficientNetB7) in the automatic classification of the disease stages. Methods: In this research, an experimental study was conducted with 416 functional magnetic resonance imaging exams from the OASIS-1 dataset, involving patients aged between 18 and 96 years. Segmented images between slices 100 and 160 were used, with standardized preprocessing and model training using the Adam optimizer (learning rate 10⁻³, batch size 32), with early stopping and adaptive learning rate reduction. Results: In the multiclass classification (No Dementia, Very Mild Dementia, Mild Dementia, and Moderate Dementia), ResNet50 achieved an accuracy of 83%, DenseNet201 82%, and VGG19 80%, standing out compared to InceptionV3 (78%) and EfficientNetB7 (72%). The binary hierarchical approach (with dementia and without dementia) showed gains in accuracy, especially ResNet50, which achieved an accuracy of 96%. Conclusion: Convolutional networks show promising performance for assisted diagnosis of Alzheimer’s Disease, especially with the adoption of hierarchical strategies that reduce errors in distinguishing the stages of the disease.
References
ABADI M, et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. In: USENIX Symposium on Operating Systems Design and Implementation (OSDI), 12., 2016, Savannah. Proceedings. Savannah: USENIX Association, 2016; 265-283.
ARCHANA B, KALIRAJAN K. Alzheimer’s disease classification using convolutional neural networks. In: International Conference on Innovative Data Communication Technologies and Application (ICIDCA), 2023. IEEE, 2023; 1044-1048.
CASTELLANI RJ, et al. Alzheimer disease: Alzheimer’s disease is the most common form of dementia. Disease-a-Month, 2010; 56(9): 484-546.
CHANDRA A, et al. Magnetic resonance imaging in Alzheimer’s disease and mild cognitive impairment. Journal of Neurology, 2019; 266(6): 1293-1302.
EBRAHIMI A, et al. Multi-class classification of Alzheimer’s disease using deep learning: a review. Current Alzheimer Research, 2021; 18(1): 50-68.
ESTEVA A, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017; 542(7639): 115-118.
GOODFELLOW I, et al. Deep learning. Cambridge: MIT Press, 2016; 800p.
GUZIOR N, et al. Alzheimer’s disease: understanding the neuropsychological profile. Neuropsychology Review, 2015; 25(3): 1-15.
HE K, et al. Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, Las Vegas. Proceedings. Los Alamitos: IEEE, 2016; 770-778.
HUANG G, et al. Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, Honolulu. Proceedings. Los Alamitos: IEEE, 2017; 4700-4708.
HUSSAIN MZ, et al. A tuned convolutional neural network model for accurate Alzheimer’s disease classification. Scientific Reports, 2025; 15: 11616.
JACK CR Jr, et al. Medial temporal atrophy on MRI in normal aging and very mild Alzheimer’s disease. Neurology, 1997; 49(3): 786-794.
LA MONTAGNE P, et al. Medial temporal lobe subregional atrophy in aging and Alzheimer’s disease: a longitudinal study. Frontiers in Aging Neuroscience, 2021; 13: 750154.
LIU M, et al. A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. NeuroImage: Clinical, 2020; 27: 102303.
LITJENS G, et al. A survey on deep learning in medical image analysis. Medical Image Analysis, 2017; 42: 60-88.
MARCUS DS, et al. Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience, 2007; 19(9): 1498-1507.
MISHRA RP, et al. Alzheimer’s disease classification using convolutional neural network and random hyperparameter tuning. In: International Conference on Odisha on Electrical Power Engineering, Communication and Computing Technology (ODICON), 3., 2024, Bhubaneswar. Proceedings. Piscataway: IEEE, 2024; 1-6.
MORRIS JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology, 1993; 43(11): 2412-2414.
PASCANU R, et al. Understanding the difficulty of training deep feedforward neural networks. In: International Conference on Machine Learning (ICML), 30., 2013, Atlanta. Proceedings. Atlanta: JMLR, 2013; 1310-1318.
PENG L, et al. The role of demographic features in predicting Alzheimer’s disease: a machine learning approach. Frontiers in Neuroscience, 2020; 14: 749.
RUBIN EH, et al. Mapping Mini-Mental State Examination scores onto Clinical Dementia Rating categories in Alzheimer’s disease. American Journal of Geriatric Psychiatry, 1998; 6(2): 149-155.
SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition. arXiv 2014; 1409: 1556.
SZEGEDY C, et al. Rethinking the inception architecture for computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, Las Vegas. Proceedings. Los Alamitos: IEEE, 2016; 2818-2826.
TAN M, LE QV. EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning (ICML), 36., 2019, Long Beach. Proceedings. Long Beach: JMLR, 2019; 6105-6114.
VAN DER MOLEN S. Scikit-learn for machine learning. Journal of Machine Learning Research, 2020; 21(1): 1-5.
ZHANG Z, et al. Evaluation metrics for machine learning in Alzheimer’s disease: a review. Journal of Alzheimer’s Disease, 2018; 62(3): 987-1002.
ZHU W, et al. Deep learning for medical image analysis: a comprehensive review. Medical Image Analysis, 2019; 58: 101552.