An IoT-based deep learning frame work for early assessment of pneumonia.

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dc.contributor.authorMADANI Rihab
dc.date.accessioned2025-11-02T10:10:18Z
dc.date.available2025-11-02T10:10:18Z
dc.date.issued2024
dc.description.abstractThe COVID-19 pandemic has highlighted the importance of remote patient monitoring for the well-being of both patients and healthcare providers. Pneumonia, a leading cause of morbidity and mortality in children, requires prompt diagnosis and treatment due to its impact on the small air sacs in the lungs. Chest X-rays are a common diagnostic tool for detecting pneumonia. This study emphasizes the development of a support tool to assist healthcare professionals in enhancing the accuracy and efficiency of pneumonia diagnosis through advanced deep learning approaches. The best model accuracy of VGGNet of 95.89% confirms that the model is robust however MobileNet can perform well in real-world situations.
dc.identifier.urihttps://dspace.univ-oran1.dz/handle/123456789/4791
dc.language.isoen
dc.subjectPneumonia, Internet of Medical Things (IoMT), Artificial intelligence, Deep learning, Edge computing, Cloud computing.
dc.titleAn IoT-based deep learning frame work for early assessment of pneumonia.
dc.typeThesis
grade.ExaminateurAIT SI LARBI Yasmine, MCB, University Of Oran, ISTA
grade.GradeMaster
grade.PrésidentILES Nadia, Professeur, University Of Oran, ISTA
grade.RapporteurDAHANE Amine, MCA, University Of Oran, ISTA
la.SpécialitéInstrumentation and Metrology
la.coteIM06
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