From Pixels to Diagnosis: AI-Powered CNN for Pneumonia Detection

Pneumonia is a major respiratory infection and is one of the top causes of death among young children around the world. Traditional diagnostic approaches, including X-ray imaging, require expert analysis and are often limited by delayed intervention and misdiagnosis, especially in resource-limited settings. In this study, we propose a convolutional neural network (CNN)-based model to facilitate the automatic identification of pneumonia from chest X-rays. The model was trained on a dataset of 5,863 pediatric X-ray images and achieved an accuracy of 93%, precision of 91%, recall of 96%, and F1-score of 94%. To optimize performance, multiple preprocessing steps were performed, including grayscale conversion, image resizing, normalizing, and data augmentation. Future work should focus on scaling the model by using larger datasets and incorporating transfer learning to amplify performance and generalizability even further.