T. Ruchitha , P. Tanuja, N. Aditya and P. Sravanthi
Pneumonia remains a significant global health concern, especially among pediatric populations, necessitating accurate and efficient diagnostic methods. This paper presents a novel approach utilizing deep learning techniques for pneumonia detection from chest X-ray images. Leveraging a dataset from the Guangzhou Women and Children's Medical Center, our convolutional neural network (CNN)-based model demonstrates robust performance in distinguishing between normal and pneumonia-afflicted X-ray images. We integrate transfer learning methodologies and ensemble learning strategies to enhance model adaptability and diagnostic accuracy, addressing challenges such as overlapping abnormalities. The proposed system, implemented within a Flask web application, offers a user-friendly interface for real-time diagnosis, bridging the gap between AI-driven diagnostics and clinical practice. Our study contributes to the advancement of pneumonia detection methodologies, emphasizing the potential of AI-powered technologies in improving diagnostic workflows and patient outcomes.
Pneumonia detection, Chest X-ray images, Deep learning, Convolutional neural networks, Transfer learning, Ensemble learning, Diagnostic accuracy