Pneumonia detection: A comprehensive study of diverse neural network architectures using chest X-rays

Akbar, Wajahat; Soomro, Abdullah; Hussain, Altaf; Hussain, Tariq; Ali, Farman; Haq, Muhammad Inam Ul; Attar, Raaz Waheeb; Alhomoud, Ahmed; AlZubi, Ahmad Ali; Alsagri, Reem

Title: Pneumonia detection: A comprehensive study of diverse neural network architectures using chest X-rays

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AMCS, volume 34 (2024)

Abstract:

Pneumonia is of deep concern in healthcare worldwide, being the most deadly infectious disease, especially among children. Chest radiographs are crucial for detecting it. However, certain vulnerable groups exhibit heightened susceptibility, emphasizing the critical nature of accurate diagnosis and timely intervention. This paper presents convolutional neural network (CNN) models for the detection of pneumonia from chest X-rays images. Among 20 different CNN models, we identified EfficientNet-B0 as the most accurate and efficient, boasting an impressive accuracy rate of 94.13%. Furthermore, the precision, recall, and F-score metrics for this model stand at 93.50%, 92.99%, and 93.14%, respectively. This research underscores the potential of CNNs to revolutionize pneumonia diagnosis.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:87183

DOI:

10.61822/amcs-2024-0045

Pages:

679-699

Source:

AMCS, volume 34, number 4 (2024) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

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Biblioteka Uniwersytetu Zielonogórskiego

Subject and keywords:

pneumonia detection CNN models chest X-ray medical imaging

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Aug 5, 2025

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Aug 5, 2025

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https://zbc.uz.zgora.pl/repozytorium/publication/101895

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International Journal of Applied Mathematics and Computer Science (AMCS)

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Pneumonia detection: A comprehensive study of diverse neural network architectures using chest X-rays	Aug 5, 2025

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