Alzheimer’s Detection through 3D Convolutional Neural Networks


  • Ryan Hogan St. John's University
  • Christoforos Christoforou



Alzheimer's Disease, Convolutional Neural Network, Medical Imaging, 3D MRI Scans, Binary Classification, Deep Learning


To inform a proper diagnosis and understanding of Alzheimer’s Disease (AD), deep learning has emerged as an alternate approach for detecting physical brain changes within magnetic resonance imaging (MRI). The advancement of deep learning within biomedical imaging, particularly in MRI scans, has proven to be an efficient resource for abnormality detection while utilizing convolutional neural networks (CNN) to perform feature mapping within multilayer perceptrons. In this study, we aim to test the feasibility of using three-dimensional convolutional neural networks to identify neurophysiological degeneration in the entire-brain scans that differentiate between AD patients and controls. In particular, we propose and train a 3D-CNN model to classify between MRI scans of cognitively-healthy individuals and AD patients. We validate our proposed model on a large dataset composed of more than seven hundred MRI scans (half AD). Our results show a validation accuracy of 79% which is at par with the current state-of-the-art. The benefits of our proposed 3D network are that it can assist in the exploration and detection of AD by mapping the complex heterogeneity of the brain, particularly in the limbic system and temporal lobe. The goal of this research is to measure the efficacy and predictability of 3D convolutional networks in detecting the progression of neurodegeneration within MRI brain scans of HC and AD patients.




How to Cite

Hogan, R., & Christoforou, C. (2021). Alzheimer’s Detection through 3D Convolutional Neural Networks. The International FLAIRS Conference Proceedings, 34.



Main Track Proceedings