CNN Algorithm Based Pneumonia Recognition With Real-Time Patient Monitoring

Computer Engineering

Pneumonia is a prevalent and potentially severe lung infection that primarily targets the respiratory system. This condition is marked by the inflammation and infection of the air sacs within the lungs, occurring in either one or both. This results in the manifestation of symptoms like coughing, elevated body temperature, labored breathing, and chest discomfort. Pneumonia continues to pose a significant worldwide public health challenge, leading to significant levels of illness and death. Therefore, it is imperative to identify pneumonia swiftly and precisely to ensure effective patient care and enhance overall results. Moreover, for every disease the process of detecting that disease at the very early stage is crucial with accurate technology. pneumonia detection is necessary not only for individual patient well-being but also for public health, epidemiological tracking, and healthcare resource management. So we propose a comprehensive solution for pneumonia detection using Convolutional Neural Networks (CNNs) and an integrated website for real-time patient status monitoring. In addition to pneumonia detection, we have developed a user-friendly website that allows healthcare providers to access patient information and X-ray results with a simple button press. This web-based platform provides real-time updates on patient status, streamlining communication and decision-making within healthcare facilities. It augments collaboration among medical staff, reduces response times, and ultimately improves the quality of care delivered to pneumonia patients. Our CNN-based algorithm leverages Deep Learning techniques to analyze chest X-ray images with high accuracy and speed. By training the model on a diverse dataset of X-ray images, it can effectively identify signs of pneumonia, providing quick and reliable results. This automated approach significantly enhances the diagnostic process, enabling healthcare professionals to make timely decisions and improve patient outcomes.

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