Enhanced Pneumonia Diagnosis Through Deep Learning

Enhanced Pneumonia Diagnosis Through Deep Learning

T. Ruchitha , P. Tanuja, N. Aditya and P. Sravanthi

T. Ruchitha , P. Tanuja, N. Aditya and P. Sravanthi "Enhanced Pneumonia Diagnosis Through Deep Learning" Published in International Journal of Trend in Research and Development (IJTRD), ISSN: 2394-9333, Volume-11 | Issue-2 , April 2024, URL: http://www.ijtrd.com/papers/IJTRD28325.pdf

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

Volume-11 | Issue-2 , April 2024


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