Veuillez utiliser cette adresse pour citer ce document : http://dspace1.univ-tlemcen.dz/handle/112/21181
Titre: Infection detection: Bridging the gap from pixels to malaria diagnosis
Auteur(s): Mahdi, Oumaima
Maghboune, Fadoua
Mots-clés: malaria, cell images, detection, LSTM.
Date de publication: 19-jui-2023
Editeur: University of Tlemcen
Résumé: Malaria, a serious disease that affects millions of people worldwide, continues to pose a significant global health challenge. remains a major global health challenge. In this context, we present a new method for detecting malaria in images of blood cells using long-term memory (LSTM) networks. Our primary goal is to develop an automated diagnostic system that can assist specialists in accurately identifying infected and uninfected blood cells. We have successfully captured complex sequence relationships within these images, which leads to high-resolution detection results. The achieved performance of our model reached an impressive accuracy of 98%, confirming its effectiveness in detecting malaria.
URI/URL: http://dspace1.univ-tlemcen.dz/handle/112/21181
Collection(s) :Master en Génie Biomedical

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