International Research Journal of Education and Technology

Pneumonia is a critical respiratory infection that significantly contributes to global morbidity and mortality, particularly among children, elderly individuals, and immunocompromised patients. According to the World Health Organization, pneumonia remains one of the leading causes of death worldwide, emphasizing the urgent need for early and accurate diagnosis. Conventional diagnostic procedures rely heavily on chest radiography interpretation by skilled radiologists. However, manual assessment is time-intensive, subject to inter-observer variability, and may lead to delayed detection,especially in resource-limited healthcare settings. To address these challenges, this study proposes an automated pneumonia detection framework based on deep learning techniques applied to chest X-ray images. between normal and pneumonia-infected lungs. Extensive preprocessing techniques, including image normalization, resizing, and data augmentation, are implemented to enhance model generalization and reduce overfitting. The model is trained and validated using a labeled chest X-ray dataset, and its performance is evaluated using standard classification metrics such as accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results demonstrate that the proposed deep learning model achieves superior diagnostic performance compared to traditional machine learning approaches. The findings indicate that deep learning-based automated systems can serve as reliable decision-support tools for clinicians, improving diagnostic efficiency and reducing workload in healthcare institutions. This research highlights the potential of artificial intelligence in medical image analysis and its capability to assist in early-stage pneumonia detection, ultimately contributing to improved patient outcomes and reduced mortality rates.

Keywords: Pneumonia detection using deep learning focuses on the application of advanced artificial intelligence techniques for automated analysis of chest X-ray images. This research area involves convolutional neural networks (CNN), transfer learning, and medical image processing to improve computer-aided diagnosis (CAD) systems in healthcare.