Pneumonia Disease Detection System

Artificial intelligence and machine learning

Pneumonia is a severe respiratory infection that can result in life-threatening complications if not diagnosed and treated promptly. Early detection of pneumonia can significantly improve patient outcomes, reduce mortality rates, and alleviate the burden on healthcare systems. This thesis presents the development and evaluation of a Pneumonia Disease Detection System (PDDS) using advanced machine learning techniques and medical imaging. The proposed system leverages chest X-ray images to automatically identify pneumonia, providing an efficient, accurate, and scalable solution for early diagnosis. This thesis explores data preprocessing, model training, evaluation, and deployment aspects, ensuring robustness and reliability. Extensive experimentation demonstrates the system’s efficacy, achieving competitive accuracy and precision metrics compared to existing methods. The proposed PDDS has the potential to transform pneumonia diagnosis, especially in resource-limited settings.

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