Artificial neural network approach to detect COVID-19 disease from X-ray images

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dc.contributor.authorHichem, Laboukhi
dc.contributor.authorAbdelhakim, Benamara
dc.contributor.authorSayad, Lamri: Rapporteur
dc.date.accessioned2024-07-10T13:28:18Z
dc.date.available2024-07-10T13:28:18Z
dc.date.issued2023
dc.description.abstractThis research employs artificial neural networks, specifically deep learning techniques, to detect COVID-19 from chest X-ray images. The study created and assessed a neural network model using a dataset of medical images from both COVID-19 infected and non-infected individuals. The results demonstrated the model's effectiveness in accurately distinguishing between infected and non-infected cases, showcasing its potential as a valuable diagnostic tool for COVID-19. The study emphasizes the necessity for ongoing research, expanding datasets, and refining neural network models to enhance accuracy. It underscores the significant potential of artificial intelligence and deep learning in disease diagnosis, promoting continuous collaboration between researchers and healthcare institutions to address global health challenges.
dc.identifier.urihttps://dspace.univ-msila.dz/handle/123456789/43582
dc.language.isoen
dc.publisherMohamed Boudiaf University of M'sila
dc.subjectartificial neural networks
dc.subjectCOVID-19 detection
dc.subjectchest X-ray images
dc.subjectdeep learning techniques
dc.subjectintelligent diagnostic tools
dc.subjectdataset
dc.subjectinfected cases
dc.subjectnon-infected cases
dc.subjectaccuracy
dc.subjectreliability
dc.subjectinnovations
dc.subjectpatient care
dc.subjectMEDICINE::Microbiology, immunology, infectious diseases
dc.subjectartificial intelligence
dc.subjectglobal health challenges
dc.titleArtificial neural network approach to detect COVID-19 disease from X-ray images
dc.typeThesis

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