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Ai-Enabled Pneumonia Detection System

dc.contributor.authorOgbe, Blessing Okwudo
dc.date.accessioned2022-02-02T08:53:39Z
dc.date.available2022-02-02T08:53:39Z
dc.date.issued2021-05-10
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/123456789/5018
dc.description2021 Computer Science Masters Thesesen_US
dc.description.abstractThe world is currently facing the global pandemic of the Coronavirus disease (COVID-19) disrupting large part of the world, the number of patients has grown progressively. For dealing with this serious emergency, accurate diagnosis and fast reporting are two significant mechanisms. The conventional medical system is facing many challenges, such as inefficient diagnosis procedure, uncoordinated management, lacking of a fast reporting and response platform. We aim to implement an AI-Enabled Real-time software and hardware platform for Pneumonia detection and diagnosis. We propose in this work a smart biomedical detection/diagnosis system. Particularly, we design a smart software. Furthermore, we develop a deep learning (DL)-based system for automatic segmentation and diagnosis. Based on the proposed system, users can conveniently upload their chest X-ray images for automatic diagnosis and returning timely results.en_US
dc.description.sponsorshipAUSTen_US
dc.language.isoenen_US
dc.publisherAUSTen_US
dc.subjectDeep Learningen_US
dc.subjectCNNen_US
dc.subjectImage Classificationen_US
dc.subjectPneumonia Detectionen_US
dc.subjectAi-Enabled Pneumonia Detection Systemen_US
dc.subjectProf. Ben Abderazek Abdallahen_US
dc.subjectOgbeh Blessing Okwudoen_US
dc.titleAi-Enabled Pneumonia Detection Systemen_US
dc.typeThesisen_US


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  • Computer Science105

    This collection contains Computer Science Student's Theses from 2009-2022

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