An Efficient CNN Framework for Radiologist level Pneumonia Detection Using Chest X-ray Images

The categorization of X-ray images using Convolutional Neural Networks (CNN) is a crucial step in the investigation of medical imaging. To help with illness diagnosis and therapy, this work entails identifying and categorizing aberrant characteristics or patterns shown in X-ray pictures. This method uses convolutional filters to teach a deep learning model to automatically extract pertinent characteristics from a huge collection of X-ray pictures. The photos are then classified into several categories, such as normal or abnormal, or particular illness categories, using the extracted characteristics. The model’s performance is assessed using a number of measures, including accuracy, loss. It has the potential to considerably increase the precision and effectiveness of illness detection in clinical settings to 92.83% accurate. X-ray image categorization using CNNs has demonstrated encouraging results in the proposed research.