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Presenter: Malek Elgadi
Supervisor:

Date: Mon, October 24, 2022
Time: 10:00:00 - 11:00:00
Place: via Zoom - please see link below

ABSTRACT

Zoom meeting link: 

https://uvic.zoom.us/j/87954264897?pwd=bGl6OUcyN0g4UFVGVXhtMHBsY0hDUT09

Meeting ID: 879 5426 4897
Password: 210125
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Meeting ID: 879 5426 4897
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ABSTRACT:

Since the end of 2019, the coronavirus disease (COVID-19) has spread rapidly worldwide. The immediate and accurate diagnosis of COVID-19 is essential for improving the prognosis of this disease and reduce the epidemic spread. Although the PCR test is the standard test for the diagnosing, radiography techniques such as chest X-rays and computed tomography (CT) scans are preferred for detection of COVID-19 disease. Deep learning and convolutional neural Networks (CNNs) play an important role in the early and accurate detection of COVID-19 using radiography images. In this project, a deep convolutional neural network framework based on a transfer learning technique with fine-tuning is used for the detection and classification of COVID-19. Two pre-trained models i.e., VGG16 and DenseNet201 are trained using COVID-19 CT images dataset. Various experiments are performed to evaluate the performance of the pre-trained models using evaluation parameters, i.e, accuracy, recall, precision, F1-score, and Area Under the Curve (AUC). The results show that the best accuracy of 99.4%, recall of 99.39%, precision of 99.4%, F1-score of 99.39%, and Area Under the Curve (AUC) of 99.93% is achieved by VGG-16 model.