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Title: :  Detecting Of SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence
PaperId: :  15581
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 7 Issue 6 2021
DUI:    16.0415/IJARIIE-15581
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Madhuri ThoteNBN Sinhgad School of Engineering Pune, Maharashtra, India
Sonali SethiNBN Sinhgad School of Engineering Pune, Maharashtra, India

Abstract

Computer Engineering
Artificial intelligence, COVID-19, coronavirus, SARS-CoV-2, deep learning, chest X-ray, imbalanced data, small data.
Chest radiographs (X-rays) combined with Deep Convolutional Neural Network (CNN) methods have been demonstrated to detect and diagnose the onset of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). However, questions remain regarding the accuracy of those methods as they are often challenged by limited datasets, performance legitimacy on imbalanced data, and have their results typically reported without proper confidence intervals. Considering the opportunity to address these issues, in this study, we propose and test six modified deep learning models, including VGG16, InceptionResNetV2, ResNet50, MobileNetV2, ResNet101, and VGG19 to detect SARSCoV-2 infection from chest X-ray images. Results are evaluated in terms of accuracy, precision, recall, and f- score using a small and balanced dataset (Study One), and a larger and imbalanced dataset (Study Two). With 95% confidence interval, VGG16 and MobileNetV2 show that, on both datasets, the model could identify patients with COVID-19 symptoms with an accuracy of up to 100%. We also present a pilot test of VGG16 models on a multi-class dataset, showing promising results by achieving 91% accuracy in detecting COVID-19, normal, and Pneumonia patients. Furthermore, we demonstrated that poorly performing models in Study One (ResNet50 and ResNet101) had their accuracy rise from 70% to 93% once trained with the comparatively larger dataset of Study Two. Still, models like InceptionResNetV2 and VGG19’s demonstrated an accuracy of 97% on both datasets, which posits the effectiveness of our proposed methods, ultimately presenting a reasonable and accessible alternative to identify patients with COVID-19.

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IJARIIE Madhuri Thote, and Sonali Sethi. "Detecting Of SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence" International Journal Of Advance Research And Innovative Ideas In Education Volume 7 Issue 6 2021 Page 230-236
MLA Madhuri Thote, and Sonali Sethi. "Detecting Of SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence." International Journal Of Advance Research And Innovative Ideas In Education 7.6(2021) : 230-236.
APA Madhuri Thote, & Sonali Sethi. (2021). Detecting Of SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence. International Journal Of Advance Research And Innovative Ideas In Education, 7(6), 230-236.
Chicago Madhuri Thote, and Sonali Sethi. "Detecting Of SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence." International Journal Of Advance Research And Innovative Ideas In Education 7, no. 6 (2021) : 230-236.
Oxford Madhuri Thote, and Sonali Sethi. 'Detecting Of SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence', International Journal Of Advance Research And Innovative Ideas In Education, vol. 7, no. 6, 2021, p. 230-236. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Detecting_Of_SARS_CoV_2_From_Chest_X_Ray_Using_Artificial_Intelligence_ijariie15581.pdf (Accessed : 18 August 2024).
Harvard Madhuri Thote, and Sonali Sethi. (2021) 'Detecting Of SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence', International Journal Of Advance Research And Innovative Ideas In Education, 7(6), pp. 230-236IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Detecting_Of_SARS_CoV_2_From_Chest_X_Ray_Using_Artificial_Intelligence_ijariie15581.pdf (Accessed : 18 August 2024)
IEEE Madhuri Thote, and Sonali Sethi, "Detecting Of SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence," International Journal Of Advance Research And Innovative Ideas In Education, vol. 7, no. 6, pp. 230-236, Nov-Dec 2021. [Online]. Available: https://ijariie.com/AdminUploadPdf/Detecting_Of_SARS_CoV_2_From_Chest_X_Ray_Using_Artificial_Intelligence_ijariie15581.pdf [Accessed : 18 August 2024].
Turabian Madhuri Thote, and Sonali Sethi. "Detecting Of SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 7 number 6 (18 August 2024).
Vancouver Madhuri Thote, and Sonali Sethi. Detecting Of SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2021 [Cited : 18 August 2024]; 7(6) : 230-236. Available from: https://ijariie.com/AdminUploadPdf/Detecting_Of_SARS_CoV_2_From_Chest_X_Ray_Using_Artificial_Intelligence_ijariie15581.pdf
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