The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias
Chest radiography is a critical tool in the early detection, management planning, and follow-up evaluation of COVID-19 pneumonia; however, in smaller clinics around the world, there is a shortage of radiologists to analyze large number of examinations especially performed during a pandemic. Limited...
| Main Authors: | , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media SA
2020
|
| Online Access: | https://hdl.handle.net/1721.1/127829 |
| _version_ | 1826193376817446912 |
|---|---|
| author | Elgendi, Mohamed Nasir, Muhammad Umer Tang, Qunfeng Fletcher, Richard R Howard, Newton Menon, Carlo Ward, Rabab Parker, William Nicolaou, Savvas |
| author2 | Massachusetts Institute of Technology. Device Research Laboratory |
| author_facet | Massachusetts Institute of Technology. Device Research Laboratory Elgendi, Mohamed Nasir, Muhammad Umer Tang, Qunfeng Fletcher, Richard R Howard, Newton Menon, Carlo Ward, Rabab Parker, William Nicolaou, Savvas |
| author_sort | Elgendi, Mohamed |
| collection | MIT |
| description | Chest radiography is a critical tool in the early detection, management planning, and follow-up evaluation of COVID-19 pneumonia; however, in smaller clinics around the world, there is a shortage of radiologists to analyze large number of examinations especially performed during a pandemic. Limited availability of high-resolution computed tomography and real-time polymerase chain reaction in developing countries and regions of high patient turnover also emphasizes the importance of chest radiography as both a screening and diagnostic tool. In this paper, we compare the performance of 17 available deep learning algorithms to help identify imaging features of COVID19 pneumonia. We utilize an existing diagnostic technology (chest radiography) and preexisting neural networks (DarkNet-19) to detect imaging features of COVID-19 pneumonia. Our approach eliminates the extra time and resources needed to develop new technology and associated algorithms, thus aiding the front-line healthcare workers in the race against the COVID-19 pandemic. Our results show that DarkNet-19 is the optimal pre-trained neural network for the detection of radiographic features of COVID-19 pneumonia, scoring an overall accuracy of 94.28% over 5,854 X-ray images. We also present a custom visualization of the results that can be used to highlight important visual biomarkers of the disease and disease progression. |
| first_indexed | 2024-09-23T09:38:26Z |
| format | Article |
| id | mit-1721.1/127829 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:38:26Z |
| publishDate | 2020 |
| publisher | Frontiers Media SA |
| record_format | dspace |
| spelling | mit-1721.1/1278292022-09-30T15:53:26Z The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias Elgendi, Mohamed Nasir, Muhammad Umer Tang, Qunfeng Fletcher, Richard R Howard, Newton Menon, Carlo Ward, Rabab Parker, William Nicolaou, Savvas Massachusetts Institute of Technology. Device Research Laboratory MIT Edgerton Center Chest radiography is a critical tool in the early detection, management planning, and follow-up evaluation of COVID-19 pneumonia; however, in smaller clinics around the world, there is a shortage of radiologists to analyze large number of examinations especially performed during a pandemic. Limited availability of high-resolution computed tomography and real-time polymerase chain reaction in developing countries and regions of high patient turnover also emphasizes the importance of chest radiography as both a screening and diagnostic tool. In this paper, we compare the performance of 17 available deep learning algorithms to help identify imaging features of COVID19 pneumonia. We utilize an existing diagnostic technology (chest radiography) and preexisting neural networks (DarkNet-19) to detect imaging features of COVID-19 pneumonia. Our approach eliminates the extra time and resources needed to develop new technology and associated algorithms, thus aiding the front-line healthcare workers in the race against the COVID-19 pandemic. Our results show that DarkNet-19 is the optimal pre-trained neural network for the detection of radiographic features of COVID-19 pneumonia, scoring an overall accuracy of 94.28% over 5,854 X-ray images. We also present a custom visualization of the results that can be used to highlight important visual biomarkers of the disease and disease progression. 2020-10-07T16:08:57Z 2020-10-07T16:08:57Z 2020-08 2020-06 2020-10-07T12:03:53Z Article http://purl.org/eprint/type/JournalArticle 2296-858X https://hdl.handle.net/1721.1/127829 Elgendi, Mohamed et al. "The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias." Frontiers in Medicine 7 (August 2020): 550 © 2020 The Authors en http://dx.doi.org/10.3389/fmed.2020.00550 Frontiers in Medicine Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Frontiers Media SA Frontiers |
| spellingShingle | Elgendi, Mohamed Nasir, Muhammad Umer Tang, Qunfeng Fletcher, Richard R Howard, Newton Menon, Carlo Ward, Rabab Parker, William Nicolaou, Savvas The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias |
| title | The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias |
| title_full | The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias |
| title_fullStr | The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias |
| title_full_unstemmed | The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias |
| title_short | The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias |
| title_sort | performance of deep neural networks in differentiating chest x rays of covid 19 patients from other bacterial and viral pneumonias |
| url | https://hdl.handle.net/1721.1/127829 |
| work_keys_str_mv | AT elgendimohamed theperformanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT nasirmuhammadumer theperformanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT tangqunfeng theperformanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT fletcherrichardr theperformanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT howardnewton theperformanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT menoncarlo theperformanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT wardrabab theperformanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT parkerwilliam theperformanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT nicolaousavvas theperformanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT elgendimohamed performanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT nasirmuhammadumer performanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT tangqunfeng performanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT fletcherrichardr performanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT howardnewton performanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT menoncarlo performanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT wardrabab performanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT parkerwilliam performanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias AT nicolaousavvas performanceofdeepneuralnetworksindifferentiatingchestxraysofcovid19patientsfromotherbacterialandviralpneumonias |