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Paper details
Number 4 - December 2024
Volume 34 - 2024
Pneumonia detection: A comprehensive study of diverse neural network architectures using chest X-rays
Wajahat Akbar, Abdullah Soomro, Altaf Hussain, Tariq Hussain, Farman Ali, Muhammad Inam Ul Haq, Raaz Waheeb Attar, Ahmed Alhomoud, Ahmad Ali AlZubi, Reem Alsagri
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.
Keywords
pneumonia detection, CNN models, chest X-ray, medical imaging