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

• Autorzy: Akbar, Wajahat , Soomro, Abdullah , Hussain, Altaf , Hussain, Tariq , Ali, Farman , Haq, Muhammad Inam Ul , Attar, Raaz Waheeb , Alhomoud, Ahmed , AlZubi, Ahmad Ali , Alsagri, Reem
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

PL

zapalenie płuc; rentgen klatki piersiowej; obrazowanie medyczne

EN

pneumonia detection; CNN model; chest X-ray; medical imaging

• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2024
• Tom: Vol. 34, no. 4
• Strony: 679--699
• Opis fizyczny: Bibliogr. 67 poz., rys., tab., wykr.

Twórcy

autor

Akbar, Wajahat

• School of Electronic and Control Engineering, Chang’an University, Beilin, Xi’an, Shaanxi, 710061, China

autor

Soomro, Abdullah

• Department of Computer Science, The Islamia University of Bahawalpur, Rabia Hall Rd, Bahawalpur, 63100, Pakistan

autor

Hussain, Altaf

• Department of Computer Science and Bioinformatics, Khushal Khan Khattak University Karak, Methawalah, Karak, 27200, Pakistan

autor

Hussain, Tariq

• School of Computer Science and Technology, Zhejiang Gongshang University, 18 Xuezheng St., Hangzhou, 310018, China,

autor

Ali, Farman

• Department of Applied AI, Sungkyunkwan University, Seoul, 03063, South Korea

autor

Haq, Muhammad Inam Ul

• Department of Computer Science and Bioinformatics, Khushal Khan Khattak University Karak, Methawalah, Karak, 27200, Pakistan

autor

Attar, Raaz Waheeb

• Management Department, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

autor

Alhomoud, Ahmed

• Department of Computer Science, Northern Border University, Arar, 91431, Saudi Arabia

autor

AlZubi, Ahmad Ali

• Department of Computer Science, King Saud University, Riyadh, 11495, Saudi Arabia

autor

Alsagri, Reem

• Department of Software Engineering, University of Hafr Al Batin, Hafar Al Batin, 39524, Saudi Arabia

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Identyfikatory

DOI

10.61822/amcs-2024-0045