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Article

COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities

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School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA
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Department of Computer Science, University of Memphis, Memphis, TN 38152, USA
3
Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA
*
Authors to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2020, 2(4), 490-504; https://doi.org/10.3390/make2040027
Received: 28 September 2020 / Revised: 22 October 2020 / Accepted: 27 October 2020 / Published: 29 October 2020
The outbreak of COVID-19 has caused more than 200,000 deaths so far in the USA alone, which instigates the necessity of initial screening to control the spread of the onset of COVID-19. However, screening for the disease becomes laborious with the available testing kits as the number of patients increases rapidly. Therefore, to reduce the dependency on the limited test kits, many studies suggested a computed tomography (CT) scan or chest radiograph (X-ray) based screening system as an alternative approach. Thereby, to reinforce these approaches, models using both CT scan and chest X-ray images need to develop to conduct a large number of tests simultaneously to detect patients with COVID-19 symptoms. In this work, patients with COVID-19 symptoms have been detected using eight distinct deep learning techniques, which are VGG16, InceptionResNetV2, ResNet50, DenseNet201, VGG19, MobilenetV2, NasNetMobile, and ResNet15V2, using two datasets: one dataset includes 400 CT scan and another 400 chest X-ray images. Results show that NasNetMobile outperformed all other models by achieving an accuracy of 82.94% in CT scan and 93.94% in chest X-ray datasets. Besides, Local Interpretable Model-agnostic Explanations (LIME) is used. Results demonstrate that the proposed models can identify the infectious regions and top features; ultimately, it provides a potential opportunity to distinguish between COVID-19 patients with others. View Full-Text
Keywords: chest X-ray; COVID-19; CT scan; deep learning; explainable AI; image processing; radiography; SARS-CoV-2; small data chest X-ray; COVID-19; CT scan; deep learning; explainable AI; image processing; radiography; SARS-CoV-2; small data
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MDPI and ACS Style

Ahsan, M.M.; Gupta, K.D.; Islam, M.M.; Sen, S.; Rahman, M.L.; Shakhawat Hossain, M. COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities. Mach. Learn. Knowl. Extr. 2020, 2, 490-504. https://doi.org/10.3390/make2040027

AMA Style

Ahsan MM, Gupta KD, Islam MM, Sen S, Rahman ML, Shakhawat Hossain M. COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities. Machine Learning and Knowledge Extraction. 2020; 2(4):490-504. https://doi.org/10.3390/make2040027

Chicago/Turabian Style

Ahsan, Md Manjurul, Kishor Datta Gupta, Mohammad Maminur Islam, Sajib Sen, Md. Lutfar Rahman, and Mohammad Shakhawat Hossain. 2020. "COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities" Machine Learning and Knowledge Extraction 2, no. 4: 490-504. https://doi.org/10.3390/make2040027

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