Accuracy and Interpretability: Developing a Computer-Aided Diagnosis System for Pneumonia Detection in Chest X-Ray Images

People of all ages can contract pneumonia, a deadly respiratory illness that is more common in underdeveloped countries. For successful treatment and higher survival rates, pneumonia must be accurately and quickly diagnosed. While chest X-ray imaging is a frequently used diagnostic technique for detecting pneumonia, its interpretation can be arbitrary and error-prone. In this study, we used a convolutional neural network (CNN) architecture to construct a computer-aided diagnostic system for pneumonia identification in chest X-ray images. Our dataset comprises 5,863 anterior-posterior view X-ray pictures that have been divided into two categories: normal and pneumonia. The photos were chosen from the Guangzhou Women and Children's Medical Center's pediatric patients between the ages of one and five, and they were first checked for quality control before being judged by two highly qualified doctors. A third expert also reviewed the assessment set to verify correctness. For the test set, our CNN model performed well, with 99% accuracy, 99% precision, 99.25% f1-score, and 98% specificity. To improve the interpretability of our model, we also included explainable AI techniques like Grad-CAM and Grad-CAM++. These approaches offer insights into CNN's decision-making process. Our computer-aided diagnosis system offers a trustworthy and unbiased method for detecting pneumonia in chest X-ray pictures, which can help with the quick identification and treatment of this potentially fatal condition.