Please use this identifier to cite or link to this item: http://dspace.univ-guelma.dz/jspui/handle/123456789/13254
Title: La segmentation d’organes à partir d’images médicales
Authors: BOULEFRAKH, INES
Keywords: Images CT, Tomodensitométrie, Covid-19, Apprentissage en profon- deur, Apprentissage auto-supervisé, Segmentation, Classification.
Issue Date: 2022
Publisher: Université de Guelma
Abstract: The spread of the infectious disease "Covid-19" has disrupted health and the global economy. There are several methods for the detection and diagnosis of this disease ; however, these are time-consuming, not available in all facilities, and expensive for the middle-class citizen. For this, researchers are trying to find a good alternative to these methods. In this context, computed tomography has paved the way for the detection of COVID-19 by processing digital medical images using artificial intelligence techniques. In this project we have adopted the techniques of deep learning and self-supervised learning with the aim of building a system for detecting Covid-19 disease in CT images of a suspected patient and locating the regions infected with the virus if the lat- ter and sick. This system will allow us to facilitate the task of detecting and screening for this disease and make them available to doctors or radiologists without making any effort or wasting time.
URI: http://dspace.univ-guelma.dz/jspui/handle/123456789/13254
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