Deep Learning Approaches for Early Detection of Alzheimer's Disease Using MRI Scans
DOI:
https://doi.org/10.5281/ijurd.v1i2.54Keywords:
Alzheimer’s Disease, Deep Learning, MRI Analysis, 3D CNN, Attention Mechanism, Neuroimaging, Early DiagnosisAbstract
Alzheimer’s disease is a progressive neurodegenerative disorder that significantly impacts cognitive functions and quality of life. Early detection remains a major challenge due to subtle structural changes in brain imaging. This study presents a comprehensive deep learning-based framework for early diagnosis using structural MRI scans. A novel three-dimensional convolutional neural network integrated with attention mechanisms is proposed to capture spatial dependencies and highlight critical brain regions. The model is trained on the ADNI dataset with preprocessing steps including normalization, skull stripping, and augmentation. Experimental results demonstrate an accuracy of 94.2%, outperforming baseline models. The system also provides interpretability through attention maps, enabling clinicians to understand model predictions. The findings suggest strong potential for real-world clinical deployment.References
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Copyright (c) 2025 Nitin Verma, Shweta Bhatia, Vivek Bajaj
This work is licensed under a Creative Commons Attribution 4.0 International License.
