<![CDATA[Pneumonia is a lung infection that is a leading cause of death, especially in children and adults in developing countries. The diagnosis of pneumonia is usually made through physical examination and interpretation of chest X-rays, but the results can vary depending on the experience of the doctor, potentially leading to misdiagnosis. This study uses a convolutional neural network (CNN) to detect pneumonia in X-ray images, with additional feature processing methods, such as the Prewitt operator to handle class imbalance. The goal is to improve the accuracy of pneumonia detection so that it can assist medical personnel in decision making and reduce misdiagnosis. As a result, the developed model achieved an accuracy of 96.59% on training data with consistent improvement, demonstrating the potential of CNN in supporting pneumonia diagnosis more accurately and reliably.]]>
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