Please use this identifier to cite or link to this item: http://dspace.univ-temouchent.edu.dz/handle/123456789/2862
Title: Detecting Covid in chest X-ray using neural networks
Authors: MANKOURI, Amani
ZENASNI, Elhachemi
Keywords: Covid detection, X-rays, Artificial intelligence, Machine learning, Deep learning, Convolutional Neural Network, Flask, TensorFlow, Flask, Python, Html, CSS, JS.
Issue Date: 2022
Citation: https://theses.univ-temouchent.edu.dz/opac_css/doc_num.php?explnum_id=4891
Abstract: The covid-19 virus infected the world's population, the doctors found themselves exhausted, and could not treat all this large number of patients, which led to great loss of human life. The use of artificial intelligence and deep learning techniques help doctors perform medical diagnosis, allowing them to treat a greater number of patients and minimizing the error rate due to fatigue. We have proposed deep artificial neural network architectures, allowing a rapid and efficient detection and localization of regions affected by covid-19 in chest X-ray images. A web platform has been implemented to allow patients to make a pre-diagnosis at any time. This project will not only help patients, but it will also help doctors to make the diagnosis.
URI: http://dspace.univ-temouchent.edu.dz/handle/123456789/2862
Appears in Collections:Informatique

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