dc.contributor.author | Ogbe, Blessing Okwudo | |
dc.date.accessioned | 2022-02-02T08:53:39Z | |
dc.date.available | 2022-02-02T08:53:39Z | |
dc.date.issued | 2021-05-10 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/123456789/5018 | |
dc.description | 2021 Computer Science Masters Theses | en_US |
dc.description.abstract | The 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.sponsorship | AUST | en_US |
dc.language.iso | en | en_US |
dc.publisher | AUST | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | CNN | en_US |
dc.subject | Image Classification | en_US |
dc.subject | Pneumonia Detection | en_US |
dc.subject | Ai-Enabled Pneumonia Detection System | en_US |
dc.subject | Prof. Ben Abderazek Abdallah | en_US |
dc.subject | Ogbeh Blessing Okwudo | en_US |
dc.title | Ai-Enabled Pneumonia Detection System | en_US |
dc.type | Thesis | en_US |