LEVERAGING RESNET-152 AND WEB TECHNOLOGY FOR RAPID COVID-19 DIAGNOSIS FROM X-RAY IMAGE
In December 2019, the SARS-CoV-2 virus gave rise to COVID-19, which was first detected in Wuhan, China. The virus has infected over 700 million individuals on Earth. This virus can spread through direct and indirect contact, making humans vulnerable even in small places or through food consumption...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Little Lion Scientific
2024
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/47233/1/LEVERAGING%20RESNET.pdf http://ir.unimas.my/id/eprint/47233/ http://www.jatit.org/volumes/Vol102No23/13Vol102No23.pdf |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | In December 2019, the SARS-CoV-2 virus gave rise to COVID-19, which was first detected in Wuhan,
China. The virus has infected over 700 million individuals on Earth. This virus can spread through direct and
indirect contact, making humans vulnerable even in small places or through food consumption. The pandemic
highlighted challenges, including a shortage of radiologists and the time-intensive interpretation of X-ray
images, leading to discrepancies and delays. To address this, a classification model based on X-ray images
became crucial for COVID-19 identification. Proposing a web-based system integrating convolutional neural
network (CNN) models, particularly the ResNet-152 model, aims to enhance precision in monitoring and
diagnosing COVID-19. After fine-tuning a pre-trained ResNet-152 model using transfer learning on a
COVID-19 dataset and adding a classification head, a COVID-19-specific classification model is created. In
this project, the pre-trained COVID-19 ResNet-152 model achieved 86.84% accuracy, 89.95% sensitivity
and 77.27% specificity. The model is then integrated into the system, which enables healthcare professionals
to upload and receive a clear visualisation of the COVID-19 classification results via Application
Programming Interface (API) endpoints. This platform enables healthcare professionals to login, upload,
search, and classify COVID-19 diagnoses based on the uploaded X-ray pictures, providing an intuitive
interface and a user-friendly system. Leveraging advanced image processing and deep learning, the system
has the potential to expedite accurate diagnoses and alleviate the workload on healthcare professionals,
ensuring swift and accurate detection of COVID-19 cases. |
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