tetano
Editor, Senior Moderator
Sci Rep
. 2024 Jan 2;14(1):280.
doi: 10.1038/s41598-023-50742-9. Potential diagnostic application of a novel deep learning- based approach for COVID-19
Alireza Sadeghi[SUP] 1 [/SUP], Mahdieh Sadeghi[SUP] 2 [/SUP], Ali Sharifpour[SUP] 3 [/SUP], Mahdi Fakhar[SUP] 4 [/SUP], Zakaria Zakariaei[SUP] 5 [/SUP], Mohammadreza Sadeghi[SUP] 2 [/SUP], Mojtaba Rokni[SUP] 6 [/SUP], Atousa Zakariaei[SUP] 7 [/SUP], Elham Sadat Banimostafavi[SUP] 8 [/SUP], Farshid Hajati[SUP] 9 [/SUP]
Affiliations
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.
. 2024 Jan 2;14(1):280.
doi: 10.1038/s41598-023-50742-9. Potential diagnostic application of a novel deep learning- based approach for COVID-19
Alireza Sadeghi[SUP] 1 [/SUP], Mahdieh Sadeghi[SUP] 2 [/SUP], Ali Sharifpour[SUP] 3 [/SUP], Mahdi Fakhar[SUP] 4 [/SUP], Zakaria Zakariaei[SUP] 5 [/SUP], Mohammadreza Sadeghi[SUP] 2 [/SUP], Mojtaba Rokni[SUP] 6 [/SUP], Atousa Zakariaei[SUP] 7 [/SUP], Elham Sadat Banimostafavi[SUP] 8 [/SUP], Farshid Hajati[SUP] 9 [/SUP]
Affiliations
- PMID: 38167985
- DOI: 10.1038/s41598-023-50742-9
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.