Novel Comparative Study of Covid-19 Detection from X-ray and CT Scan Images Using CNN and MLP Neural Networks

Belhia Souaad; Aljahmani Souha; Bahram Tyeb; Adjoudj Reda

doi:10.55549/epstem.1409294

Conference Paper

Year 2023, Volume: 26, 26 - 37, 30.12.2023

Belhia Souaad Aljahmani Souha Bahram Tyeb Adjoudj Reda

https://doi.org/10.55549/epstem.1409294

Abstract

References

Apostolopoulos, I. D., & Bessiana, T. (2020). Covid-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 43, 635-640.
Bedad, F., Adjoudj, R., & Bousahba, N. (2022). Study of the robustness of a transformation-based multi-biometric template schemes protection, International Journal of Computing Digital Systems, 11(1), 335-344.
Belhia, S., Al Jahmani.S., & Adjoudj, R. (2022). Automatic detection of Covid-19 based in artificial intelligence tools. Turkish Journal of Computer and Mathematics Education, 13(3), 668-680.

Novel Comparative Study of Covid-19 Detection from X-ray and CT Scan Images Using CNN and MLP Neural Networks

Year 2023, Volume: 26, 26 - 37, 30.12.2023

Belhia Souaad Aljahmani Souha Bahram Tyeb Adjoudj Reda

https://doi.org/10.55549/epstem.1409294

Abstract

The coronavirus has caused the deaths of millions of people and has endangered the entire healthcare system. In order to count positive cases and stop the disease from spreading, Rapid clinical results may prevent the COVID-19 from spreading and help medical professionals treat patients while working under challenging circumstances.. Normal disease diagnosis using a laboratory test requires equipment and takes some time with the use of X-ray and chest CT Scan images, artificial intelligence techniques are extensively used to categorize the COVID-19. In this study we present an automatic detection approach for COVID-19 infection based on Chest CT and X-ray images using a Multilayer Perceptron (MLP) Neurons Network and a Convolutional Neural Network (CNN). The two models are evaluated in two classes, COVID-19 and normal images, for detection by Chest X-ray images we obtained 95,7% accuracy using MLP model and 90% accuracy using CNN model. For detection by Chest CT image we obtained, 80,60 % accuracy using the MLP model and 88,49 % accuracy using the CNN. The experimental results indicate that the proposed approach can achieve high accuracy in detecting COVID-19 from X-ray images, demonstrating the potential of using machine learning techniques in medical diagnosis.

Keywords

COVID-19 , MLP Neural Network , X-rays images , CT Scan image , Machine learning , CNN

References

Apostolopoulos, I. D., & Bessiana, T. (2020). Covid-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 43, 635-640.
Bedad, F., Adjoudj, R., & Bousahba, N. (2022). Study of the robustness of a transformation-based multi-biometric template schemes protection, International Journal of Computing Digital Systems, 11(1), 335-344.
Belhia, S., Al Jahmani.S., & Adjoudj, R. (2022). Automatic detection of Covid-19 based in artificial intelligence tools. Turkish Journal of Computer and Mathematics Education, 13(3), 668-680.

There are 3 citations in total.

Details

Primary Language	English
Subjects	Software Engineering (Other)
Journal Section	Articles
Authors	Belhia Souaad Aljahmani Souha Bahram Tyeb Adjoudj Reda
Early Pub Date	December 24, 2023
Publication Date	December 30, 2023
Published in Issue	Year 2023 Volume: 26

Cite

APA	Souaad, B., Souha, A., Tyeb, B., Reda, A. (2023). Novel Comparative Study of Covid-19 Detection from X-ray and CT Scan Images Using CNN and MLP Neural Networks. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 26, 26-37. https://doi.org/10.55549/epstem.1409294