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
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.
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.
Details
| Primary Language | English |
|---|---|
| Subjects | Software Engineering (Other) |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | December 24, 2023 |
| Publication Date | December 30, 2023 |
| Published in Issue | Year 2023 |
