Evaluating and Selecting Optimal CNN Architectures for Accurate Pneumonia Detection in Chest X-Rays

Israa Shakir Seger
International Journal of Computational and Electronic Aspects in Engineering
Volume 5: Issue 4, December 2024, pp 183-193

Author's Information
Israa Shakir Seger1 
Corresponding Author
1College of Basic Education, University of Muthanna, Muthanna, Iraq
israa.shakir@mu.edu.iq
Research Paper -- Peer Research Papered
First online on – 30 December 2024

Open Access article under Creative Commons License

Cite this article –Israa Shakir Seger “Evaluating and Selecting Optimal CNN Architectures for Accurate Pneumonia Detection in Chest X-Rays”, International Journal of Computational and Electronic Aspects in Engineering, RAME Publishers, vol. 5, Issue 4, pp. 183-193, 2024.
https://doi.org/10.26706/ijceae.5.4.20241106

Abstract:-
Pneumonia is the lungs' alveoli filling with fluid, and it mainly affects children below 5 years and adults above 65. Results: We demonstrate our approach using different configurations of convolutional neural networks (CNNs) on a chest X-ray binary classification task detecting pneumonia cases. The focus here is more on performance evaluation among various simple CNN architectures to find the one that gives least loss and highest accuracy. The ultimate aim is to enable the widespread adoption of a strong tool for the diagnosis of viral, bacterial and fungal pneumonia as well as community-acquired pneumonia based on chest X-rays only by clinicians.
Index Terms:-
Pneumonia Detection, CNNs, Chest X-rays, ANN, Neural networks
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