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DC Field | Value | Language |
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dc.contributor.author | Lati, Abdelhai | - |
dc.contributor.author | Hafiane, Maroua | - |
dc.contributor.author | Ferkhi, Kenza | - |
dc.contributor.author | Fellah, Aicha | - |
dc.contributor.author | Bazzine, Rania | - |
dc.date.accessioned | 2024-09-17T10:09:24Z | - |
dc.date.available | 2024-09-17T10:09:24Z | - |
dc.date.issued | 2024 | - |
dc.identifier.citation | FACULTE DES NOUVELLES TECHNOLOGIES DE L'INFORMATIQUE ET DE LA COMMUNICATION | en_US |
dc.identifier.uri | https://dspace.univ-ouargla.dz/jspui/handle/123456789/36706 | - |
dc.description.abstract | Tuberculosis has seen a large spread in the world and is a serious and infectious disease. We have proposed a solution for the initial diagnosis of this disease. It is a Diatub application, based on artificial intelligence and its advanced techniques of deep learning and machine learning. The method of application is by imaging the lung X-ray and lifting it to the application, after which Diatub processes the image. The result is the patient's state of health if he is infected or healthy with a percentage as this does not take long, the result can be saved so as to inform the doctor and give the final diagnosis. | en_US |
dc.language.iso | en | en_US |
dc.publisher | UNIVERSITY KASDI MERBAH OUARGLA | en_US |
dc.subject | tuberculosis | en_US |
dc.subject | artificial intelligence | en_US |
dc.subject | lung X-ray | en_US |
dc.subject | deep learning | en_US |
dc.subject | machine learning | en_US |
dc.title | Artificial Intelligence Based Tuberculosis Diagnosis | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Département d'Electronique et des Télécommunications - Master |
Files in This Item:
File | Description | Size | Format | |
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HAFIANE-FERKHI-FELLAH-BAZZINE.pdf | 1,93 MB | Adobe PDF | View/Open |
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