| RAPID AUTOMATIC DETECTION OF COVID-19 IN CHEST CT IMAGES USING VGG-19 AND TRANSFER LEARNING |
| Paper ID : 1024-GEOSPATIAL (R7) |
| Authors: |
|
Masoomeh Gomroki *1, Reza Shah-Hosseini1, Mahdi Hasanlou2 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran 2Department of Surveying and Geomatics engineering |
| Abstract: |
| This paper presents a deep learning approach for swift detection of COVID-19 in chest CT scan images in order to facilitate treatment planning and reduce the burden on hospital resources and staff workload. The detection procedure starts with a pre-processing step, which involves noise removal and resizing, and the pre-processed images are fed to VGG16, which is a powerful deep learning network for image classification applications. All algorithms have been implemented in Python and the deep learning network has been implemented in Tesorflow using the Keras library. Using VGG16, we have achieved 99% and 92% accuracy for the training and test data, respectively. Considering the accuracy of the method, it can be used for swift clinical detection of COVID-19, which could be of useful and magnificent help to treatment personnel. Also, this method is really helpful for detecting patient and starting treatment as soon as possible and reduces the cost of treatments |
| Keywords: |
| COVID-19, CT scan, deep learning, automatic detection, transfer learning |
| Status : Paper Accepted (Poster Presentation) |
