Diagnosis of COVID-19 disease using CT scan images and pre-trained models
This work addresses the need for fast and accurate COVID-19 diagnosis, but it is incremental as it applies existing pre-trained models to a new medical dataset.
The paper tackled the problem of diagnosing COVID-19 by using CT scan images and a deep learning model combining three pre-trained networks, achieving an accuracy close to 97% on a dataset of 2482 images.
Diagnosis of COVID-19 is necessary to prevent and control the disease. Deep learning methods have been considered a fast and accurate method. In this paper, by the parallel combination of three well-known pre-trained networks, we attempted to distinguish coronavirus-infected samples from healthy samples. The negative log-likelihood loss function has been used for model training. CT scan images in the SARS-CoV-2 dataset were used for diagnosis. The SARS-CoV-2 dataset contains 2482 images of lung CT scans, of which 1252 images belong to COVID-19-infected samples. The proposed model was close to 97% accurate.