The week's pick
Reseach Article
Early-Stage Alzheimer’s Disease Prediction using MRI -based Data: A Multimodal Deep Learning Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 75 |
| Year of Publication: 2025 |
| Authors: Sujan Aryal |
10.5120/ijca2025924640
|
Sujan Aryal . Early-Stage Alzheimer’s Disease Prediction using MRI -based Data: A Multimodal Deep Learning Approach. International Journal of Computer Applications. 186, 75 ( Mar 2025), 26-31. DOI=10.5120/ijca2025924640
Abstract
Alzheimer's Disease (AD) is a progressive neurological condition characterized by cognitive deterioration and memory impairment. Prompt identification is essential for timely intervention, facilitating more efficient management and treatment approaches. Conventional diagnostic approaches depend on cognitive evaluations and clinical assessments, which frequently do not identify Alzheimer's disease in its initial stages. Deep learning has emerged as an effective method for automating the diagnosis of Alzheimer's disease via brain MRI data. This study introduces a deep learning framework aimed at detecting early-stage Alzheimer’s by utilizing MRI data to improve accuracy and facilitate prompt intervention. The dataset comprises 6,400 MRI scans categorized into four different groups: Mild Demented, Moderate Demented, Non-Demented, and Very Mild Demented. Carefully comparing most of the models such as VGG16, VGG19, ResNet, MobileNetV2, InceptionV3, DenseNet169, and EfficientNetB0 and fine-tuning and mitigating overfitting custom models, the final model achieved a weighted F1 score of 0.99%, indicating potential for substantial enhancement as an optimal predictor or classifier of MRI scans and the results also suggest that its effectiveness in promoting early diagnosis for patients with Alzheimer's disease.
References
- Alzheimer's Disease Neuroimaging Initiative, "Predicting Alzheimer’s Disease Progression Using Multi-Modal Deep Learning Approach," IEEE Trans. Med. Imaging, 2022.
- K. Wang, T. Jiang, M. Liang, L. Wang, L. Tian, X. Zhang, K. Li, and Z. Liu, "Discriminative Analysis of Early Alzheimer’s Disease Using Resting-State fMRI," IEEE J. Biomed. Health Inform., 2021.
- S. Grueso and R. Viejo-Sobera, "Tensorizing GAN With High-Order Pooling for Alzheimer’s Disease Assessment," IEEE Trans. Med. Imaging, 2022.
- S. Katabathula, Q. Wang, and R. Xu, "Multimodal Discrimination of Alzheimer's Disease Based on Regional Cortical Atrophy and Hypometabolism," J. Neurosci. Methods, 2022.
- H. A. Helaly, M. M. Badawy, and A. Y. Haikal, "Predict Alzheimer’s disease using hippocampus MRI data: A lightweight 3D deep convolutional network model," Neuroinformatics, 2022.
- N. Singh, A. Y. Wang, P. Sankaranarayanan, P. T. Fletcher, and S. Joshi, "Early Prediction of Alzheimer's Disease Using Null Longitudinal Model-Based Classifiers," IEEE J. Biomed. Health Inform., 2021.
- J. E. Iglesias, J. Jiang, C. Y. Liu, and Z. Tu, "Deep Learning Approach for Early Detection of Alzheimer's Disease," Front. Aging Neurosci., 2023.
- C. Liu, "An Explainable 3D Residual Self-Attention Deep Neural Network for Joint Atrophy Localization and Alzheimer’s Disease Diagnosis Using Structural MRI," Pattern Recognit., 2022.
- J. Sheng et al., "A Practical Alzheimer’s Disease Classifier via Brain Imaging-Based Deep Learning on 85,721 Samples," IEEE Access, 2022.
- B. Li et al., "Long-Term Cognitive Decline Prediction Based on Multi-Modal Data Using Multimodal3DSiameseNet: Transfer Learning from Alzheimer’s Disease to Parkinson’s Disease," Comput. Methods Programs Biomed., 2023.
- M. Ahmadi et al., "Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging," Alzheimers Dement., 2023.
- P. Balaji et al., "PPAD: A Deep Learning Architecture to Predict Progression of Alzheimer’s Disease," Expert Syst. Appl., 2023.
- G. Mirabnahrazam et al., "Using Deep Learning Radiomics to Distinguish Cognitively Normal Adults at Risk of Alzheimer’s Disease From Normal Control," IEEE J. Biomed. Health Inform., 2023.
- Y. W. Park et al., "MRI-Based Biomarkers of Preclinical AD: An Alzheimer Signature," Neuroimage Clin., 2022.
- J. Yan, C. Song, and B. Zheng, "Identifying Individuals with Alzheimer's Disease-Like Brains Based on Structural Imaging in the Human Connectome Project Aging Cohort," Neurobiol. Aging, 2022.
Index Terms
Keywords