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https://dspace.univ-ouargla.dz/jspui/handle/123456789/34489
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DC Field | Value | Language |
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dc.contributor.author | BENKADDOUR, Mohammed Kamel | - |
dc.contributor.author | Bendehiba, Kaouthar | - |
dc.contributor.author | Habita, Atidel | - |
dc.date.accessioned | 2023-10-03T10:35:09Z | - |
dc.date.available | 2023-10-03T10:35:09Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | https://dspace.univ-ouargla.dz/jspui/handle/123456789/34489 | - |
dc.description.abstract | Alzheimer’s disease is a type of brain disease. It is a progressive disease which means it gets worse with time there is no cure and his diagnosis is a medical challenge. Therefore, early diagnosis is crucial and can help to improve symptoms significantly. As technology advances, deep learning techniques have recently achieved great success in medical image analysis. This project aims to develop a method of Alzheimer's disease diagnosis using MRI images, which can distinguish medical images of the brain to help doctors to classify and predict Alzheimer's disease. This is based on deep learning with convolutional neural networks (CNN) used to predict Alzheimer from the Kaggle dataset. Experiments results have given encouraging prediction and accuracy in comparison with other work cited in related works. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Kasdi Marbah University Ouargla | en_US |
dc.subject | Alzheimer’s Disease | en_US |
dc.subject | Brain | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Medical Image | en_US |
dc.subject | MRI | en_US |
dc.subject | CNN | en_US |
dc.subject | Dataset | en_US |
dc.subject | Prediction | en_US |
dc.title | Efficient Image Classification for Early Prediction of Alzheimer's Disease | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Département d'informatique et technologie de l'information - Master |
Files in This Item:
File | Description | Size | Format | |
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BENDEHIBA-HABITA.pdf | 4,97 MB | Adobe PDF | View/Open |
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