Journal Article

Potential diagnostic application of a novel deep learning- based approach for COVID-19

Fakhar, Mahdi, Sadeghi, Alireza, Banimostafavi, Elham Sadat, Hajati, Farshid, Sadeghi, Mahdieh, Sharifpour, Ali, Zakariaei, Zakaria, Rokni, Mojtaba, Sadeghi, Mohammadreza, Zakariaei, Atousa

2024

Overview

COVID-19 is a highly communicable respiratory illness caused by the novel coronavirus SARS-CoV-2, which has had a significant impact on global public health and the economy. Detecting COVID-19 patients during a pandemic with limited medical facilities can be challenging, resulting in errors and further complications. Therefore, this study aims to develop deep learning models to facilitate automated diagnosis of COVID-19 from CT scan records of patients. The study also introduced COVID-MAH-CT, a new dataset that contains 4442 CT scan images from 133 COVID-19 patients, as well as 133 CT scan 3D volumes. We proposed and evaluated six different transfer learning models for slide-level analysis that are responsible for detecting COVID-19 in multi-slice spiral CT. Additionally, multi-head attention squeeze and excitation residual (MASERes) neural network, a novel 3D deep model was developed for patient-level analysis, which analyzes all the CT slides of a given patient as a whole and can accurately diagnose COVID-19. The codes and dataset developed in this study are available at https://github.com/alrzsdgh/COVID . The proposed transfer learning models for slide-level analysis were able to detect COVID-19 CT slides with an accuracy of more than 99%, while MASERes was able to detect COVID-19 patients from 3D CT volumes with an accuracy of 100%. These achievements demonstrate that the proposed models in this study can be useful for automatically detecting COVID-19 in both slide-level and patient-level from patients’ CT scan records, and can be applied for real-world utilization, particularly in diagnosing COVID-19 cases in areas with limited medical facilities.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/114 / 631/136 / 631/61 / 639/166 / Computed tomography / Coronaviruses / COVID-19 / COVID-19 - diagnostic imaging / COVID-19 Testing / Deep Learning / Health facilities / Humanities and Social Sciences / Humans / Medical imaging / multidisciplinary / Neural networks / Neural Networks, Computer / Pandemics / Patients / Public health / SARS-CoV-2 / Science / Science (multidisciplinary) / Severe acute respiratory syndrome coronavirus 2 / Tomography, X-Ray Computed - methods / Transfer learning