Creator:

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

Contributor:

Woźniak, Marcin - ed. ; Kumar, Yogesh - ed. ; Ijaz, Muhammad Fazal - ed.

Title:

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

Subtitle:

.

Group publication title:

AMCS, volume 34 (2024)

Subject and Keywords:

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

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

Date:

2024

Resource Type:

artykuł

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

Rights:

Biblioteka Uniwersytetu Zielonogórskiego