CNN based Pneumonia detection

CNN-based pneumonia detection utilizes convolutional neural networks to analyze medical imaging data, particularly chest X-rays, for the identification of pneumonia. The process begins with preprocessing the images to enhance features relevant to pneumonia, followed by the application of various convolutional layers that automatically extract hierarchical features from the images. These features are then fed into fully connected layers, which classify the images as either showing signs of pneumonia or being healthy. The advantage of using CNNs lies in their ability to learn from large datasets, enabling high accuracy and efficiency in diagnosing pneumonia, often matching or surpassing the performance of human radiologists. This technology not only accelerates the diagnostic process but also assists in triaging patients in clinical settings, ultimately contributing to improved patient outcomes.