Accuracy Improvement of Convolutional Neural Network with Ghost Weight Normalization for Pneumonia Classification

Pneumonia is a critical respiratory condition that requires accurate and timely diagnosis to ensure effective treatment. In this study, we propose the integration of Ghost Weight Normalization (GWN) into a Convolutional Neural Network (CNN) to enhance the accuracy and performance of pneumonia detection. The dataset used was derived from the Kaggle repository, comprising 5,856 chest X-ray images divided into two classes: Normal and Pneumonia. The CNN + GWN model demonstrated improved classification metrics with an accuracy, precision, recall, and F1-score of 95%, outperforming the CNN-Based model, which achieved 92%. While the CNN + GWN model required slightly longer training time and more epochs to achieve its best performance, the trade-off resulted in more robust and reliable predictions. The enhanced performance is attributed to the ability of GWN to normalize weights effectively, providing diverse normalization variations and improving training stability. These results underscore the potential of the CNN + GWN model for reliable pneumonia detection and highlight its capability to address the limitations of conventional CNN architectures.