TY - JOUR
T1 - Pneumonia Detection
T2 - A Comprehensive Study of Diverse Neural Network Architectures using Chest X-Rays
AU - Akbar, Wajahat
AU - Soomro, Abdullah
AU - Hussain, Altaf
AU - Hussain, Tariq
AU - Ali, Farman
AU - Haq, Muhammad Inam Ul
AU - Attar, Raaz Waheeb
AU - Alhomoud, Ahmed
AU - Alzubi, Ahmad Ali
AU - Alsagri, Reem
N1 - Publisher Copyright:
© 2024 Wajahat Akbar et al., published by Sciendo.
PY - 2024/9/1
Y1 - 2024/9/1
N2 - 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.
AB - 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.
KW - CNN models
KW - chest X-ray
KW - medical imaging
KW - pneumonia detection
UR - https://www.scopus.com/pages/publications/85213575624
U2 - 10.61822/amcs-2024-0045
DO - 10.61822/amcs-2024-0045
M3 - Article
AN - SCOPUS:85213575624
SN - 1641-876X
VL - 34
SP - 679
EP - 699
JO - International Journal of Applied Mathematics and Computer Science
JF - International Journal of Applied Mathematics and Computer Science
IS - 4
ER -
