TY  - JOUR
T1  - Novel Comparative Study of Covid-19 Detection from X-ray and CT Scan Images Using CNN and MLP Neural Networks
AU  - Souaad, Belhia
AU  - Souha, Aljahmani
AU  - Tyeb, Bahram
AU  - Reda, Adjoudj
PY  - 2023
DA  - December
DO  - 10.55549/epstem.1409294
JF  - The Eurasia Proceedings of Science Technology Engineering and Mathematics
JO  - EPSTEM
PB  - ISRES Publishing
WT  - DergiPark
SN  - 2602-3199
SP  - 26
EP  - 37
VL  - 26
LA  - en
AB  - 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.
KW  - COVID-19
KW  - MLP Neural Network
KW  - X-rays images
KW  - CT Scan image
KW  - Machine learning
KW  - CNN
CR  - 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.
CR  - 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.
CR  - 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.
UR  - https://doi.org/10.55549/epstem.1409294
L1  - https://dergipark.org.tr/en/download/article-file/3617947
ER  -