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2D/3D Convolutional Neural Networks for Alzheimer's Disease Prediction using Brain MRI Image
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 41 |
| Year of Publication: 2022 |
| Authors: Sunil Kumar D.S., Manju H., Harshitha R., Bharath K.N., Ganesh Kumar M.T., Kiran |
10.5120/ijca2022922481
|
Sunil Kumar D.S., Manju H., Harshitha R., Bharath K.N., Ganesh Kumar M.T., Kiran . 2D/3D Convolutional Neural Networks for Alzheimer's Disease Prediction using Brain MRI Image. International Journal of Computer Applications. 184, 41 ( Dec 2022), 7-9. DOI=10.5120/ijca2022922481
Abstract
The symptoms of Alzheimer's disease (AD) include significant memory loss and cognitive decline. It is linked to major alterations in brain structure that can be seen by magnetic resonance imaging (MRI) scans. Utilizing image classification technologies like convolutional neural networks, the visible preclinical structural alterations offer a chance for AD early identification (CNN). The sample size of the majority of AD-related studies, however, is currently a limitation. It is crucial to find a productive technique to train an image classifier with little data. In our project, we investigated various CNN-based transfer-learning techniques for MRI brain structure AD prediction. We discover that the prediction performance was enhanced when compared to a deep CNN trained from scratch by both pretrained 2D AlexNet with a 2D-representation approach and simple neural networks with a pre-trained 3D autoencoder.The pretrained 2D AlexNet performed even better (86%) than the 3D CNN with autoencoder (77%).
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