Deep Learning-Based Pneumonia Detection Using Chest X-Ray Images | ||||
| International Journal of Telecommunications | ||||
| Volume 05, Issue 01, January 2025, Page 1-16 PDF (1.57 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijt.2025.346338.1071 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
M.B. Badawi   1; Baher Salah2; Wael Fawaz3
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| 1Department of Artificial Intelligence, College of Information Technology, Misr University for Science and Technology (MUST), 6th of October City 12566, Egyp | ||||
| 21 Department of Computer Science and Engineering, Faculty of Computing, King Salman International University | ||||
| 3Department of Computer Science, Faculty of Computers and Information, Suez University, Suez, Egypt | ||||
| Abstract | ||||
| This research presents a deep learning approach designed for the automated detection of pneumonia through the analysis of chest X-ray images. Pneumonia remains the foremost in-fectious cause of mortality in children under five years old, leading to the deaths of 740,180 children globally in 2019. This statistic represents 14% of all deaths within this demographic. This alarming statistic underscores the necessity for improved diagnostic methods, particularly in environments with limited resources where specialized radiologists may not be readily available. In this study we present a deep learning methodology aimed at automating the identification of pneumonia through the analysis of chest X-ray images, specifically focusing on a dataset comprised of5,863 images categorized as either pneumonia or normal. This dataset, obtained from retrospective cohorts of pediatric patients between the ages of one and five years at the Guangzhou Women and Children’s Medical Center, was subjected to stringent quality control measures to eliminate low-quality images, ensuring high diagnostic accuracy. This methodology includes an extensive preprocessing pipeline featuring resizing, grayscale conversion, normalization, and data augmentation techniques to enhance the robustness of the model. The architecture employed consists of a Convolutional Neural Network (CNN) with seven convolutional blocks designed to obtain hierarchical characteristics from the input images. The model achieved an accuracy of 97%, with precision and recall values of 0.975 and 0.977 respectively, indicating its efficacy in distinguishing between pneumonia and normal cases. The analysis of the model’s performance was further substantiated through confusion matrices and detailed classification reports. | ||||
| Keywords | ||||
| Deep Learning; Pneumonia Detection; Chest X-Ray; CNN; Medical Imaging | ||||
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