Autor:

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

Współtwórca:

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

Tytuł:

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

Podtytuł:

.

Tytuł publikacji grupowej:

AMCS, volume 34 (2024)

Temat i słowa kluczowe:

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2024

Typ zasobu:

artykuł

DOI:

10.61822/amcs-2024-0045

Strony:

679-699

Źródło:

AMCS, volume 34, number 4 (2024) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego