Optimizing InceptionV3 through Network Pruning for Lung Disease Detection in Chest X-Ray Images

Abstract

Lung disease remains a major global health concern, and accurate diagnosis using chest X-ray images plays a crucial role in supporting effective clinical decision-making. The contribution of this work lies in empirically demonstrating how internal redundancy removal through standard magnitude-based pruning can improve both performance and stability of an established CNN architecture. The COVID-Qu-Ex dataset was utilized, consisting of 11,956 COVID, 11,263 pneumonia, and 10,701 normal X-ray images. All models were trained under identical preprocessing, augmentation, and evaluation protocols, with three different random seeds to ensure result stability and reproducibility. Experimental results show that the pruned InceptionV3 model achieved superior performance, with an accuracy of 95.54% ± 0.0041, precision of 95.62% ± 0.0039, recall of 95.54% ± 0.0041, and F1-score of 95.54% ± 0.0041, outperforming the baseline InceptionV3 as well as other modern CNN architectures such as ResNet101 and VGG19. These findings demonstrate that network pruning can effectively reduce model redundancy while maintaining, and even improving, classification performance. Future work will focus on extending the proposed approach to other CNN architectures, integrating interpretability techniques, and addressing data imbalance issues to enhance clinical reliability.