Abstract
Alzheimer’s disease is a progressive neurodegenerative condition that affects millions of people worldwide, causing memory loss, cognitive decline, and eventual loss of independence. Early detection is essential for slowing its progression and improving patient quality of life. This project presents a deep learning-based system for the automatic detection of Alzheimer’s disease using medical imaging data, specifically MRI scans. Leveraging convolutional neural networks (CNNs), the model is trained to distinguish between healthy individuals and patients at different stages of the disease. It learns to identify subtle spatial and structural abnormalities in brain images that often go unnoticed in traditional analysis. The framework is developed and validated using benchmark datasets such as ADNI, ensuring robust and reliable performance. Experimental results show high accuracy, sensitivity, and specificity, highlighting the effectiveness of deep learning in detecting early signs of Alzheimer’s. This approach represents a promising advancement toward automated, non-invasive, and efficient diagnosis, supporting clinicians in early intervention and personalized treatment planning.
Keywords
Alzheimer, convolutional neural networks (CNNs), MRI, deep learning, ADNI
Citations
IRE Journals:
Syeda Jamala Fatima, Dr. Omar khan Durrani "Alzheimer's Disease Prediction Using Machine Learning" Iconic Research And Engineering Journals Volume 9 Issue 3 2025 Page 210-214 https://doi.org/10.64388/IREV9I3-1710498-4358
IEEE:
Syeda Jamala Fatima, Dr. Omar khan Durrani
"Alzheimer's Disease Prediction Using Machine Learning" Iconic Research And Engineering Journals, 9(3) https://doi.org/10.64388/IREV9I3-1710498-4358
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