DIAGNOSIS OF ALZHEIMER'S DISEASE BY THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK USING UNSUPERVISED FEATURE LEARNING METHOD
A. Soliman, S., A. El-Dahshan, E., M. Salem, A. (2022). DIAGNOSIS OF ALZHEIMER'S DISEASE BY THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK USING UNSUPERVISED FEATURE LEARNING METHOD. EKB Journal Management System, 22(1), 1-15. doi: 10.21608/ijicis.2021.80596.1103
Sarah A. Soliman; El-Sayed A. El-Dahshan; Abdel-Badeeh M. M. Salem. "DIAGNOSIS OF ALZHEIMER'S DISEASE BY THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK USING UNSUPERVISED FEATURE LEARNING METHOD". EKB Journal Management System, 22, 1, 2022, 1-15. doi: 10.21608/ijicis.2021.80596.1103
A. Soliman, S., A. El-Dahshan, E., M. Salem, A. (2022). 'DIAGNOSIS OF ALZHEIMER'S DISEASE BY THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK USING UNSUPERVISED FEATURE LEARNING METHOD', EKB Journal Management System, 22(1), pp. 1-15. doi: 10.21608/ijicis.2021.80596.1103
A. Soliman, S., A. El-Dahshan, E., M. Salem, A. DIAGNOSIS OF ALZHEIMER'S DISEASE BY THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK USING UNSUPERVISED FEATURE LEARNING METHOD. EKB Journal Management System, 2022; 22(1): 1-15. doi: 10.21608/ijicis.2021.80596.1103

DIAGNOSIS OF ALZHEIMER'S DISEASE BY THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK USING UNSUPERVISED FEATURE LEARNING METHOD

International Journal of Intelligent Computing and Information Sciences
Article 1, Volume 22, Issue 1, February 2022, Page 1-15  XML PDF (835.65 K)
Document Type: Original Article
DOI: 10.21608/ijicis.2021.80596.1103
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Authors
Sarah A. Soliman email 1; El-Sayed A. El-Dahshan2; Abdel-Badeeh M. M. Salemorcid 3
1Higher Technological Institure,Cairo,Egypt
2Egyptian E-learning University, Cairo,Egypt
3Computer Sciece Department, Faculty of Computer and Information Sciences, Ain Shams University
Abstract
The rise of Deep Learning in the past two decades has prompted research into solutions to help improve Alzheimer’s diagnosis based on neuroimaging data. As such, a wide variety of different techniques have been used, but a clear turn towards the use of Convolutional Neural Networks (CNN) has been observed in the last decade. To effectively predicate Alzheimer's Disease (AD), this paper proposed a two stage method. The first stage involves learning the best representation of the training data using an improved sparse autoencoder (SAE), an unsupervised neural network. The second stage involves using a 3D-Convolutional Neural Network (3D-CNN) to differentiate between the health status and diseased status based on the learned records and MRI scan of the brain. The SAE was optimized so as to train an efficient model. We report on experiments using the ADNI data set involving 897 historical scans. We demonstrate that using 3D convolutional neural networks with sparse auto encoder outperform several other classifiers stated in the literature.
Keywords
Alzheimer's disease (AD); Sparse autoencoder (SAE); 3D-Convolutional Neural Network (3D-CNN); Feature Learning
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