Diagnosis of COVID-19 disease using CT scan images and pre-trained models

TL;DR

This paper presents a deep learning approach using combined pre-trained models to accurately diagnose COVID-19 from lung CT scans, achieving nearly 97% accuracy on a dataset of 2482 images.

Contribution

It introduces a novel parallel combination of three pre-trained networks for COVID-19 diagnosis from CT images, utilizing negative log-likelihood loss for training.

Findings

Achieved approximately 97% accuracy in COVID-19 detection

Used a dataset of 2482 lung CT scan images

Demonstrated effectiveness of combined pre-trained models

Abstract

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.